├── .github └── workflows │ └── static.yml ├── .gitignore ├── LICENSE ├── README.md ├── data_extraction ├── build_html.py ├── build_latex.py ├── create_plots.py ├── data_extraction.py ├── data_source.yaml ├── latex │ ├── 3dgs_compression_survey.tex │ ├── 3dgs_survey_text.tex │ ├── 3dgs_table_compression.tex │ ├── 3dgs_table_densification.tex │ ├── datasets_and_evaluation_statistics.tex │ ├── habbrv.bst │ ├── survey.bib │ └── tex_templates │ │ ├── contributions.tex │ │ ├── entry.tex │ │ ├── figure.tex │ │ └── table.tex ├── preprocess_images.py └── vis │ ├── README.md │ ├── all_cols_corr.pdf │ ├── heatmap_hist_plot.py │ ├── most_cols_hist.pdf │ ├── opacity_scale_hist.pdf │ ├── relevant_cols_corr.pdf │ ├── treemap_plot.pdf │ ├── treemap_plot.png │ └── treemap_plot_ply.py ├── datasets.bib ├── methods.bib ├── methods ├── chen2024hac.md ├── chen2025fcgs.md ├── chen2025hac-plus.md ├── cheng2024gaussianpro.md ├── fan2024lightgaussian.md ├── fang2024minisplatting.md ├── feng2024flashgs.md ├── girish2024eagles.md ├── hu2024gsplat.md ├── jung2024relaxing ├── kim2024CVPR.md ├── lee2024compact.md ├── lee2025compression3dgaussiansplatting.md ├── li2024mvgsplatting.md ├── liu2024atomgs.md ├── liu2024compgs.md ├── liu2024hemgs.md ├── lu2024scaffold.md ├── morgenstern2024compact.md ├── navaneet2023compact3d.md ├── niedermayr2024compressed.md ├── papantonakis2024reducing.md ├── pateux2025bogauss.md ├── ren2024octreegs.md ├── seo2024flod.md ├── sun2024f3dgs.md ├── taming20243dgs.md ├── wang2024contextgs.md ├── wang2024end.md ├── wu2024implicit.md ├── xie2024mesongs.md ├── yan2024multiscale.md ├── yang2024spectrally.md ├── zhang2024fregs.md ├── zhang2024gaussianspa.md └── zhang2024pixelgs.md ├── methods_compression.bib ├── methods_densification.bib ├── plots ├── DeepBlending_compaction_LPIPS.pdf ├── DeepBlending_compaction_LPIPS_legend.pdf ├── DeepBlending_compaction_LPIPS_legend_h.pdf ├── DeepBlending_compaction_PSNR.pdf ├── DeepBlending_compaction_PSNR_legend.pdf ├── DeepBlending_compaction_PSNR_legend_h.pdf ├── DeepBlending_compaction_SSIM.pdf ├── DeepBlending_compaction_SSIM_legend.pdf ├── DeepBlending_compaction_SSIM_legend_h.pdf ├── DeepBlending_compression_LPIPS.pdf ├── DeepBlending_compression_LPIPS_legend.pdf ├── DeepBlending_compression_LPIPS_legend_h.pdf ├── DeepBlending_compression_PSNR.pdf ├── DeepBlending_compression_PSNR_legend.pdf ├── DeepBlending_compression_PSNR_legend_h.pdf ├── DeepBlending_compression_SSIM.pdf ├── DeepBlending_compression_SSIM_legend.pdf ├── DeepBlending_compression_SSIM_legend_h.pdf ├── MipNeRF360_compaction_LPIPS.pdf ├── MipNeRF360_compaction_LPIPS_legend.pdf ├── MipNeRF360_compaction_LPIPS_legend_h.pdf ├── MipNeRF360_compaction_PSNR.pdf ├── MipNeRF360_compaction_PSNR_legend.pdf ├── MipNeRF360_compaction_PSNR_legend_h.pdf ├── MipNeRF360_compaction_SSIM.pdf ├── MipNeRF360_compaction_SSIM_legend.pdf ├── MipNeRF360_compaction_SSIM_legend_h.pdf ├── MipNeRF360_compression_LPIPS.pdf ├── MipNeRF360_compression_LPIPS_legend.pdf ├── MipNeRF360_compression_LPIPS_legend_h.pdf ├── MipNeRF360_compression_PSNR.pdf ├── MipNeRF360_compression_PSNR_legend.pdf ├── MipNeRF360_compression_PSNR_legend_h.pdf ├── MipNeRF360_compression_SSIM.pdf ├── MipNeRF360_compression_SSIM_legend.pdf ├── MipNeRF360_compression_SSIM_legend_h.pdf ├── SyntheticNeRF_compression_LPIPS.pdf ├── SyntheticNeRF_compression_LPIPS_legend.pdf ├── SyntheticNeRF_compression_LPIPS_legend_h.pdf ├── SyntheticNeRF_compression_PSNR.pdf ├── SyntheticNeRF_compression_PSNR_legend.pdf ├── SyntheticNeRF_compression_PSNR_legend_h.pdf ├── SyntheticNeRF_compression_SSIM.pdf ├── SyntheticNeRF_compression_SSIM_legend.pdf ├── SyntheticNeRF_compression_SSIM_legend_h.pdf ├── TanksAndTemples_compaction_LPIPS.pdf ├── TanksAndTemples_compaction_LPIPS_legend.pdf ├── TanksAndTemples_compaction_LPIPS_legend_h.pdf ├── TanksAndTemples_compaction_PSNR.pdf ├── TanksAndTemples_compaction_PSNR_legend.pdf ├── TanksAndTemples_compaction_PSNR_legend_h.pdf ├── TanksAndTemples_compaction_SSIM.pdf ├── TanksAndTemples_compaction_SSIM_legend.pdf ├── TanksAndTemples_compaction_SSIM_legend_h.pdf ├── TanksAndTemples_compression_LPIPS.pdf ├── TanksAndTemples_compression_LPIPS_legend.pdf ├── TanksAndTemples_compression_LPIPS_legend_h.pdf ├── TanksAndTemples_compression_PSNR.pdf ├── TanksAndTemples_compression_PSNR_legend.pdf ├── TanksAndTemples_compression_PSNR_legend_h.pdf ├── TanksAndTemples_compression_SSIM.pdf ├── TanksAndTemples_compression_SSIM_legend.pdf └── TanksAndTemples_compression_SSIM_legend_h.pdf ├── project-page ├── index_template.html └── static │ ├── css │ ├── bulma-carousel_purged.min.css │ ├── bulma-slider_purged.min.css │ ├── bulma_purged.min.css │ ├── datatables.min.css │ ├── index.css │ └── style.css │ ├── images │ ├── Eurographics_2025_Logo.png │ ├── chen2024hac.pdf │ ├── chen2024hac.png │ ├── chen2024hac_h250px.webp │ ├── chen2024hac_h500px.webp │ ├── chen2025fcgs.pdf │ ├── chen2025fcgs.png │ ├── chen2025fcgs_h250px.webp │ ├── chen2025fcgs_h500px.webp │ ├── chen2025hac-plus.pdf │ ├── chen2025hac-plus.png │ ├── chen2025hac-plus_h250px.webp │ ├── chen2025hac-plus_h500px.webp │ ├── cheng2024gaussianpro.pdf │ ├── cheng2024gaussianpro.png │ ├── cheng2024gaussianpro_h250px.webp │ ├── cheng2024gaussianpro_h500px.webp │ ├── fan2024lightgaussian.pdf │ ├── fan2024lightgaussian.png │ ├── fan2024lightgaussian_h250px.webp │ ├── fan2024lightgaussian_h500px.webp │ ├── fang2024minisplatting.pdf │ ├── fang2024minisplatting.png │ ├── fang2024minisplatting_h250px.webp │ ├── fang2024minisplatting_h500px.webp │ ├── favicon.ico │ ├── feng2024flashgs.pdf │ ├── feng2024flashgs.png │ ├── feng2024flashgs_h250px.webp │ ├── feng2024flashgs_h500px.webp │ ├── girish2024eagles.pdf │ ├── girish2024eagles.png │ ├── girish2024eagles_h250px.webp │ ├── girish2024eagles_h500px.webp │ ├── hu2024gsplat.jpg │ ├── hu2024gsplat.pdf │ ├── hu2024gsplat_h250px.webp │ ├── hu2024gsplat_h500px.webp │ ├── jung2024relaxing.pdf │ ├── jung2024relaxing.png │ ├── jung2024relaxing_h250px.webp │ ├── jung2024relaxing_h500px.webp │ ├── kim2024CVPR.pdf │ ├── kim2024CVPR.png │ ├── kim2024CVPR_h250px.webp │ ├── kim2024CVPR_h500px.webp │ ├── lee2024compact.pdf │ ├── lee2024compact.png │ ├── lee2024compact_h250px.webp │ ├── lee2024compact_h500px.webp │ ├── lee2025compression3dgaussiansplatting.pdf │ ├── lee2025compression3dgaussiansplatting.png │ ├── lee2025compression3dgaussiansplatting_h250px.webp │ ├── lee2025compression3dgaussiansplatting_h500px.webp │ ├── li2024mvgsplatting.pdf │ ├── li2024mvgsplatting.png │ ├── li2024mvgsplatting_h250px.webp │ ├── li2024mvgsplatting_h500px.webp │ ├── liu2024atomgs.pdf │ ├── liu2024atomgs.png │ ├── liu2024atomgs_h250px.webp │ ├── liu2024atomgs_h500px.webp │ ├── liu2024compgs.pdf │ ├── liu2024compgs.png │ ├── liu2024compgs_h250px.webp │ ├── liu2024compgs_h500px.webp │ ├── liu2024hemgs.pdf │ ├── liu2024hemgs.png │ ├── liu2024hemgs_h250px.webp │ ├── liu2024hemgs_h500px.webp │ ├── lu2024scaffold.pdf │ ├── lu2024scaffold.png │ ├── lu2024scaffold_h250px.webp │ ├── lu2024scaffold_h500px.webp │ ├── morgenstern2024compact.pdf │ ├── morgenstern2024compact.png │ ├── morgenstern2024compact_h250px.webp │ ├── morgenstern2024compact_h500px.webp │ ├── navaneet2023compact3d.pdf │ ├── navaneet2023compact3d.png │ ├── navaneet2023compact3d_h250px.webp │ ├── navaneet2023compact3d_h500px.webp │ ├── niedermayr2024compressed.pdf │ ├── niedermayr2024compressed.png │ ├── niedermayr2024compressed_h250px.webp │ ├── niedermayr2024compressed_h500px.webp │ ├── papantonakis2024reducing.pdf │ ├── papantonakis2024reducing.png │ ├── papantonakis2024reducing_h250px.webp │ ├── papantonakis2024reducing_h500px.webp │ ├── pateux2025bogauss.pdf │ ├── pateux2025bogauss.png │ ├── pateux2025bogauss_h250px.webp │ ├── pateux2025bogauss_h500px.webp │ ├── ren2024octreegs.pdf │ ├── ren2024octreegs.png │ ├── ren2024octreegs_h250px.webp │ ├── ren2024octreegs_h500px.webp │ ├── seo2024flod.pdf │ ├── seo2024flod.png │ ├── seo2024flod_h250px.webp │ ├── seo2024flod_h500px.webp │ ├── sun2024f3dgs.jpg │ ├── sun2024f3dgs.pdf │ ├── sun2024f3dgs_h250px.webp │ ├── sun2024f3dgs_h500px.webp │ ├── survey-teaser.pdf │ ├── survey-teaser.png │ ├── survey-teaser.webp │ ├── survey-teaser_h250px.webp │ ├── survey-teaser_h500px.webp │ ├── taming20243dgs.jpg │ ├── taming20243dgs.pdf │ ├── taming20243dgs.png │ ├── taming20243dgs.webp │ ├── taming20243dgs_h250px.webp │ ├── taming20243dgs_h500px.webp │ ├── wang2024contextgs.pdf │ ├── wang2024contextgs.png │ ├── wang2024contextgs_h250px.webp │ ├── wang2024contextgs_h500px.webp │ ├── wang2024end.pdf │ ├── wang2024end.png │ ├── wang2024end_h250px.webp │ ├── wang2024end_h500px.webp │ ├── wu2024implicit.pdf │ ├── wu2024implicit.png │ ├── wu2024implicit_h250px.webp │ ├── wu2024implicit_h500px.webp │ ├── xie2024mesongs.pdf │ ├── xie2024mesongs.png │ ├── xie2024mesongs_h250px.webp │ ├── xie2024mesongs_h500px.webp │ ├── yan2024multiscale.pdf │ ├── yan2024multiscale.png │ ├── yan2024multiscale_h250px.webp │ ├── yan2024multiscale_h500px.webp │ ├── yang2024spectrally.pdf │ ├── yang2024spectrally.png │ ├── yang2024spectrally_h250px.webp │ ├── yang2024spectrally_h500px.webp │ ├── zhang2024fregs.pdf │ ├── zhang2024fregs.png │ ├── zhang2024fregs_h250px.webp │ ├── zhang2024fregs_h500px.webp │ ├── zhang2024gaussianspa.pdf │ ├── zhang2024gaussianspa.png │ ├── zhang2024gaussianspa_h250px.webp │ ├── zhang2024gaussianspa_h500px.webp │ ├── zhang2024pixelgs.pdf │ ├── zhang2024pixelgs.png │ ├── zhang2024pixelgs_h250px.webp │ └── zhang2024pixelgs_h500px.webp │ └── js │ ├── bulma-carousel.js │ ├── bulma-carousel.min.js │ ├── bulma-slider.js │ ├── bulma-slider.min.js │ ├── compare.js │ ├── datatables.min.js │ ├── fontawesome.all.min.js │ ├── index.js │ ├── plotly-1.58.5.min.js │ ├── scripts.js │ ├── vidsplit.js │ └── vidsplit_multi.js ├── requirements.txt ├── results ├── DeepBlending.csv ├── MipNeRF360.csv ├── SyntheticNeRF.csv └── TanksAndTemples.csv └── sources ├── results_kerbl20233dgs ├── DeepBlending │ ├── drjohnson.csv │ └── playroom.csv ├── MipNeRF360 │ ├── bicycle.csv │ ├── bonsai.csv │ ├── counter.csv │ ├── flowers.csv │ ├── garden.csv │ ├── kitchen.csv │ ├── room.csv │ ├── stump.csv │ └── treehill.csv ├── SyntheticNeRF │ ├── chair.csv │ ├── drums.csv │ ├── ficus.csv │ ├── hotdog.csv │ ├── lego.csv │ ├── materials.csv │ ├── mic.csv │ └── ship.csv └── TanksAndTemples │ ├── train.csv │ └── truck.csv ├── results_lee2025compression3dgaussiansplatting ├── DeepBlending │ ├── drjohnson.csv │ └── playroom.csv ├── MipNeRF360 │ ├── bicycle.csv │ ├── bonsai.csv │ ├── counter.csv │ ├── flowers.csv │ ├── garden.csv │ ├── kitchen.csv │ ├── room.csv │ ├── stump.csv │ └── treehill.csv └── TanksAndTemples │ ├── train.csv │ └── truck.csv ├── results_liu2024hemgs ├── DeepBlending │ ├── drjohnson.csv │ └── playroom.csv ├── MipNeRF360 │ ├── bicycle.csv │ ├── bonsai.csv │ ├── counter.csv │ ├── flowers.csv │ ├── garden.csv │ ├── kitchen.csv │ ├── room.csv │ ├── stump.csv │ └── treehill.csv ├── SyntheticNeRF │ ├── chair.csv │ ├── drums.csv │ ├── ficus.csv │ ├── hotdog.csv │ ├── lego.csv │ ├── materials.csv │ ├── mic.csv │ └── ship.csv └── TanksAndTemples │ ├── train.csv │ └── truck.csv └── results_pateux2025bogauss ├── DeepBlending ├── drjohnson.csv └── playroom.csv ├── MipNeRF360 ├── bicycle.csv ├── bonsai.csv ├── counter.csv ├── flowers.csv ├── garden.csv ├── kitchen.csv ├── room.csv ├── stump.csv └── treehill.csv └── TanksAndTemples ├── train.csv └── truck.csv /.github/workflows/static.yml: -------------------------------------------------------------------------------- 1 | # Simple workflow for deploying static content to GitHub Pages 2 | name: Deploy static content to Pages 3 | 4 | on: 5 | # Runs on pushes targeting the default branch 6 | push: 7 | branches: ["main"] 8 | 9 | # Allows you to run this workflow manually from the Actions tab 10 | workflow_dispatch: 11 | 12 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 13 | permissions: 14 | contents: read 15 | pages: write 16 | id-token: write 17 | 18 | # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. 19 | # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. 20 | concurrency: 21 | group: "pages" 22 | cancel-in-progress: false 23 | 24 | jobs: 25 | # Single deploy job since we're just deploying 26 | deploy: 27 | environment: 28 | name: github-pages 29 | url: ${{ steps.deployment.outputs.page_url }} 30 | runs-on: ubuntu-latest 31 | steps: 32 | - name: Checkout 33 | uses: actions/checkout@v4 34 | - name: Set up Python 35 | uses: actions/setup-python@v5 36 | - name: Install dependencies 37 | run: | 38 | python -m pip install --upgrade pip 39 | pip install pandas bibtexparser jinja2 Pillow 40 | - name: Run Custom Script 41 | run: python data_extraction/build_html.py 42 | - name: Setup Pages 43 | uses: actions/configure-pages@v5 44 | - name: Upload artifact 45 | uses: actions/upload-pages-artifact@v3 46 | with: 47 | # Upload entire repository 48 | path: './project-page/' 49 | - name: Deploy to GitHub Pages 50 | id: deployment 51 | uses: actions/deploy-pages@v4 52 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | project-page/index.html 2 | .vscode/launch.json 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[cod] 7 | *$py.class 8 | 9 | # C extensions 10 | *.so 11 | 12 | # Distribution / packaging 13 | .Python 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | wheels/ 26 | share/python-wheels/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .nox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | *.py,cover 53 | .hypothesis/ 54 | .pytest_cache/ 55 | cover/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | db.sqlite3-journal 66 | 67 | # Flask stuff: 68 | instance/ 69 | .webassets-cache 70 | 71 | # Scrapy stuff: 72 | .scrapy 73 | 74 | # Sphinx documentation 75 | docs/_build/ 76 | 77 | # PyBuilder 78 | .pybuilder/ 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | # For a library or package, you might want to ignore these files since the code is 90 | # intended to run in multiple environments; otherwise, check them in: 91 | # .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # poetry 101 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 102 | # This is especially recommended for binary packages to ensure reproducibility, and is more 103 | # commonly ignored for libraries. 104 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 105 | #poetry.lock 106 | 107 | # pdm 108 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 109 | #pdm.lock 110 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 111 | # in version control. 112 | # https://pdm.fming.dev/#use-with-ide 113 | .pdm.toml 114 | 115 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 116 | __pypackages__/ 117 | 118 | # Celery stuff 119 | celerybeat-schedule 120 | celerybeat.pid 121 | 122 | # SageMath parsed files 123 | *.sage.py 124 | 125 | # Environments 126 | .env 127 | .venv 128 | env/ 129 | venv/ 130 | ENV/ 131 | env.bak/ 132 | venv.bak/ 133 | 134 | # Spyder project settings 135 | .spyderproject 136 | .spyproject 137 | 138 | # Rope project settings 139 | .ropeproject 140 | 141 | # mkdocs documentation 142 | /site 143 | 144 | # mypy 145 | .mypy_cache/ 146 | .dmypy.json 147 | dmypy.json 148 | 149 | # Pyre type checker 150 | .pyre/ 151 | 152 | # pytype static type analyzer 153 | .pytype/ 154 | 155 | # Cython debug symbols 156 | cython_debug/ 157 | 158 | # PyCharm 159 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 160 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 161 | # and can be added to the global gitignore or merged into this file. For a more nuclear 162 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 163 | .idea/ 164 | .DS_Store 165 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Wieland Morgenstern 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /data_extraction/data_source.yaml: -------------------------------------------------------------------------------- 1 | pateux2025bogauss: 2 | url: "https://github.com/spateux/3dgs-compression-survey/tree/pateux2025bogauss/sources/results_pateux2025bogauss" 3 | is_csv: True 4 | 5 | wang2024contextgs: 6 | url: "https://github.com/wyf0912/ContextGS/tree/main/results" 7 | is_csv: True 8 | 9 | morgenstern2024compact: 10 | url: "https://github.com/fraunhoferhhi/Self-Organizing-Gaussians/tree/main/results" 11 | is_csv: True 12 | 13 | lee2024compact: 14 | url: "https://arxiv.org/src/2311.13681" 15 | is_csv: False 16 | table_names: 17 | - tab:qual1 18 | - tab:qual2 19 | 20 | navaneet2023compact3d: 21 | # url: "https://arxiv.org/src/2311.18159" 22 | url: "https://arxiv.org/src/2311.18159v2" # needs this v2 as newer version breaks latex parsing 23 | is_csv: False 24 | table_names: 25 | - tab:comp_sota 26 | filename: "arxiv.tex" 27 | 28 | fan2024lightgaussian: 29 | url: "https://arxiv.org/src/2311.17245" 30 | is_csv: False 31 | table_names: 32 | - tab:main1 33 | 34 | chen2024hac: 35 | url: "https://github.com/YihangChen-ee/HAC/tree/main/results" 36 | is_csv: True 37 | 38 | niedermayr2024compressed: 39 | url: "https://arxiv.org/src/2401.02436" 40 | is_csv: False 41 | table_names: 42 | - tab:dataset-metrics 43 | table_rotated: True 44 | filename: "sec/4_method.tex" 45 | 46 | wang2024end: 47 | url: "https://github.com/USTC-IMCL/RDO-Gaussian/tree/main/results" 48 | is_csv: True 49 | 50 | hu2024gsplat: 51 | url: "https://github.com/nerfstudio-project/gsplat/tree/main/examples/benchmarks/compression/results" 52 | is_csv: True 53 | 54 | # wu2024implicit: 55 | # url: "https://github.com/wuminye/ImplicitGS/tree/main/results" 56 | # is_csv: True 57 | 58 | xie2024mesongs: 59 | url: "https://github.com/ShuzhaoXie/MesonGS/tree/main/results" 60 | is_csv: True 61 | 62 | cheng2024gaussianpro: 63 | url: "https://github.com/kcheng1021/GaussianPro/tree/version1.0/results" 64 | is_csv: True 65 | 66 | liu2024compgs: 67 | url: "https://github.com/LiuXiangrui/CompGS/tree/main/results" 68 | is_csv: True 69 | 70 | lee2025compression3dgaussiansplatting: 71 | url: "https://github.com/w-m/3dgs-compression-survey/tree/main/sources/results_lee2025compression3dgaussiansplatting" 72 | is_csv: True 73 | 74 | kerbl3Dgaussians: 75 | url: "https://github.com/w-m/3dgs-compression-survey/tree/main/sources/results_kerbl20233dgs" 76 | is_csv: True 77 | 78 | chen2025hac-plus: 79 | url: " https://github.com/YihangChen-ee/HAC-plus/tree/main/results" 80 | is_csv: True 81 | 82 | chen2025fcgs: 83 | url: "https://github.com/YihangChen-ee/FCGS/tree/main/results" 84 | is_csv: True 85 | 86 | zhang2024gaussianspa: 87 | url: "https://github.com/noodle-lab/GaussianSpa/tree/main/results" 88 | is_csv: True 89 | 90 | liu2024hemgs: 91 | url: "https://github.com/w-m/3dgs-compression-survey/tree/main/sources/results_liu2024hemgs" 92 | is_csv: True 93 | -------------------------------------------------------------------------------- /data_extraction/latex/3dgs_compression_survey.tex: -------------------------------------------------------------------------------- 1 | \documentclass{article} 2 | 3 | % Packages 4 | \usepackage[utf8]{inputenc} % input encoding 5 | \usepackage[T1]{fontenc} % font encoding 6 | \usepackage{amsmath} % math enhancements 7 | \usepackage{amsfonts} % math fonts 8 | \usepackage{amssymb} % additional math symbols 9 | \usepackage{graphicx} % include graphics 10 | \usepackage{hyperref} % hyperlinks 11 | \usepackage{cite} % improved citations 12 | \usepackage{geometry} % page dimensions 13 | \usepackage{authblk} % for author affiliations 14 | \usepackage{float} 15 | \usepackage{booktabs} 16 | \usepackage{siunitx} 17 | \usepackage{array} 18 | \usepackage{varwidth} 19 | \usepackage[table]{xcolor} % loads also »colortbl« 20 | % colors for table 21 | \definecolor{lightred}{HTML}{FF9999} 22 | \definecolor{lightyellow}{HTML}{FFFF99} 23 | \definecolor{lightorange}{HTML}{FFCC99} 24 | 25 | \usepackage{makecell} 26 | \usepackage{adjustbox} 27 | % make text the same size even when its bold in a table 28 | \newsavebox\CBox 29 | \def\textBF#1{\sbox\CBox{#1}\resizebox{\wd\CBox}{\ht\CBox}{\textbf{#1}}} 30 | 31 | % Page dimensions 32 | \geometry{letterpaper, margin=1in} 33 | 34 | % Title, author, and date 35 | \title{3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods} 36 | \author[1]{Milena T. Bagdasarian} 37 | \author[1]{Paul Knoll} 38 | \author[3]{Yi-Hsin Li} 39 | \author[1,2]{Florian Barthel} 40 | \author[1]{Anna Hilsmann} 41 | \author[1,2]{Peter Eisert} 42 | \author[1]{Wieland Morgenstern} 43 | 44 | \affil[1]{Fraunhofer Heinrich Hertz, HHI} 45 | \affil[2]{Humboldt University of Berlin} 46 | \affil[3]{Technical University Berlin} 47 | % make sure no date is displayed, arxiv periodically rebuilds 48 | % submissions which would change the date 49 | \date{} 50 | 51 | \begin{document} 52 | 53 | \maketitle 54 | 55 | \begin{abstract} 56 | 57 | We present a work-in-progress survey on 3D Gaussian Splatting\cite{kerbl3Dgaussians} compression methods, 58 | focusing on their statistical performance across various benchmarks. This survey aims 59 | to facilitate comparability by summarizing key statistics of different compression 60 | approaches in a tabulated format. The datasets evaluated include TanksAndTemples\cite{TanksAndTemples}, 61 | MipNeRF360\cite{MipNeRF360}, DeepBlending\cite{DeepBlending}, and SyntheticNeRF\cite{SyntheticNeRF}. For each method, we report the Peak 62 | Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Learned Perceptual 63 | Image Patch Similarity (LPIPS), and the resultant size in megabytes (MB), as 64 | provided by the respective authors. 65 | This is an ongoing, open project, and we invite contributions from the research community 66 | as GitHub issues or pull requests. Please visit \mbox{\bfseries\url{http://w-m.github.io/3dgs-compression-survey/}} 67 | for more information and a sortable version of the table. 68 | 69 | \end{abstract} 70 | 71 | % \section{Introduction} 72 | % % Novel view synthesis, nerf, 3DGS ..(vs. traditional photogrammetry -> not scalable) 73 | % % objective: comprehensive overview of the current state of 3DGS compression methods 74 | % % why is compression important in 3DGS? 75 | % % applications in areas such as computer graphics, virtual reality (VR), augmented reality (AR), and real-time rendering. 76 | 77 | \section{Scope of this survey} 78 | % table; comparison of statistics of compression methods 79 | % types of compression 80 | % reproducability 81 | 82 | In this survey, we focus on compression methods for 3D Gaussian Splatting (3DGS), aiming to optimize memory usage while preserving visual quality and real-time rendering speed. We provide a comprehensive comparison of various compression techniques, with quantitative results for the most commenly used datasets summarized in a tabulated format. Our goal is to ensure transparency and reproducibility of the included approaches. Additionally, we offer a brief explanation of each pipeline and discuss main compression approaches. Rather than covering all existing 3DGS methods, our focus is specifically on their compression techniques; for a broader overview of 3DGS methods and applications, we refer readers to \cite{wu2024recent,fei20243d}. While we include many common approaches shared between neural radiance field (NeRF)\cite{mildenhall2020nerf} compression and 3DGS compression, we direct readers to \cite{li2023compressing,chen2024far} for NeRF-specific compression methods. 83 | 84 | \input{3dgs_table} 85 | \input{datasets_and_evaluation_statistics} 86 | \input{3dgs_survey_text} 87 | 88 | % Bibliography (if needed) 89 | \bibliographystyle{habbrv} 90 | \bibliography{../../methods_compression,../../methods_densification, ../../datasets, survey} % assuming you have a references.bib file 91 | 92 | \end{document} -------------------------------------------------------------------------------- /data_extraction/latex/3dgs_survey_text.tex: -------------------------------------------------------------------------------- 1 | % \section{Fundamentals of 3D Gaussian Splatting and Compression} 2 | % \subsection{3D Gaussian Splatting} 3 | 4 | % 3D Gaussian Splatting (3DGS)\cite{kerbl3Dgaussians} is a 3D scene representation based on rasterization used to perform novel view synthesis. While more traditonal methods rely on polygonal meshes or voxel grids, 3D Gaussian Splatting relys on a set of overlapping Gaussian functions (or "splats") to model the appearence of surfaces or volumes in 3D space. A "splat" in 3DGS is a 3D Gaussian ditribution that is described by its position (XYZ), Covariance (stretch and scale), color (RGB) and Alpha (transparency). 5 | % % 3D Gaussian Splatting; basic principles; usage in 3D graphics and rendering. 6 | % % Data representation in 3DGS; Gaussian splats; challanges such as data size and rendering performance 7 | % % explain need for compression; storage efficiency; computation efficiency 8 | 9 | % \section{Compression in the context of 3D Gaussian Splatting} 10 | % % What can be compressed? What do we call compression? 11 | % % spatial data, color and intensity, (temporal data - not focused on) 12 | % % densification and pruning 13 | 14 | % \section{Classification of Compression Methods} 15 | % % Vector quantization 16 | % % Anchor-based approaches 17 | % % Pruning 18 | % % ... ??? 19 | 20 | \section{Description of Included Compression Approaches} 21 | \input{3dgs_contributions.tex} 22 | % contribution summaries from table 23 | % possibly use color encoding 24 | 25 | % \section{Discussion} 26 | 27 | % \section{Future Directions} 28 | 29 | % \section{Conclusion} 30 | -------------------------------------------------------------------------------- /data_extraction/latex/3dgs_table_compression.tex: -------------------------------------------------------------------------------- 1 | \rotatebox{90}{ 2 | \begin{minipage}{\textheight} % Adjust the minipage width to fit the content 3 | \section*{Survey Table} 4 | % Have a look into ../tex_templates/preamble.tex for packages and color definitions 5 | 6 | % color every second line in grey 7 | \rowcolors{2}{gray!25}{white} 8 | 9 | \footnotesize 10 | \setlength{\tabcolsep}{3pt} 11 | 12 | % center the table 13 | \begin{adjustbox}{center} 14 | \begin{tabular}{ll|lllr|lllr|lllr|lllr} 15 | \toprule 16 | Method & Rank & \multicolumn{4}{c|}{Tanks and Temples} & \multicolumn{4}{c|}{Mip-NeRF 360} & \multicolumn{4}{c|}{Deep Blending} & \multicolumn{4}{c|}{Synthetic NeRF} \\ 17 | & \tiny & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny \makecell{Size \\ MB$\downarrow$} & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny \makecell{Size \\ MB$\downarrow$} & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny \makecell{Size \\ MB$\downarrow$} & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny \makecell{Size \\ MB$\downarrow$} \\ 18 | \midrule 19 | HAC++-highrate & \cellcolor{lightred}5.1 & 24.33 & .853 & .181 & \cellcolor{lightyellow}7.3 & \cellcolor{lightorange}27.82 & \cellcolor{lightyellow}.811 & .231 & 19.4 & \cellcolor{lightyellow}30.34 & \cellcolor{lightred}.911 & .254 & 5.5 & \cellcolor{lightred}33.76 & \cellcolor{lightred}.969 & \cellcolor{lightred}.033 & 1.9 \\ 20 | HEMGS-highrate & \cellcolor{lightorange}5.7 & \cellcolor{lightred}24.58 & \cellcolor{lightorange}.856 & \cellcolor{lightorange}.176 & 10.1 & \cellcolor{lightred}27.93 & \cellcolor{lightorange}.813 & .230 & 21.0 & \cellcolor{lightorange}30.37 & \cellcolor{lightred}.911 & .253 & 6.7 & \cellcolor{lightorange}33.71 & \cellcolor{lightorange}.968 & \cellcolor{lightyellow}.035 & 1.6 \\ 21 | HEMGS-lowrate & \cellcolor{lightyellow}6.2 & \cellcolor{lightorange}24.42 & .848 & .192 & \cellcolor{lightorange}6.0 & 27.75 & .806 & .248 & \cellcolor{lightyellow}12.5 & 30.24 & .908 & .266 & \cellcolor{lightred}3.0 & \cellcolor{lightyellow}33.58 & \cellcolor{lightyellow}.967 & .037 & \cellcolor{lightyellow}1.3 \\ 22 | HAC-highrate & 7.1 & \cellcolor{lightyellow}24.40 & .853 & \cellcolor{lightyellow}.177 & 11.8 & \cellcolor{lightyellow}27.77 & \cellcolor{lightyellow}.811 & .230 & 22.9 & \cellcolor{lightyellow}30.34 & .906 & .258 & 6.7 & \cellcolor{lightorange}33.71 & \cellcolor{lightorange}.968 & \cellcolor{lightorange}.034 & 2.0 \\ 23 | ContextGS_lowrate & 7.1 & 24.12 & .849 & .186 & 9.9 & 27.62 & .808 & .237 & 13.3 & 30.09 & .907 & .265 & \cellcolor{lightyellow}3.7 & & & & \\ 24 | HAC++-lowrate & 7.1 & 24.22 & .849 & .190 & \cellcolor{lightred}5.4 & 27.60 & .803 & .253 & \cellcolor{lightred}8.7 & 30.16 & .907 & .266 & \cellcolor{lightorange}3.1 & 32.77 & .965 & .041 & \cellcolor{lightred}0.8 \\ 25 | HAC-lowrate & 7.7 & 24.04 & .846 & .187 & 8.5 & 27.53 & .807 & .238 & 16.0 & 29.98 & .902 & .269 & 4.6 & 33.24 & \cellcolor{lightyellow}.967 & .037 & \cellcolor{lightorange}1.2 \\ 26 | gsplat-1.00M & 7.8 & 24.03 & \cellcolor{lightred}.857 & \cellcolor{lightred}.163 & 16.1 & 27.29 & \cellcolor{lightyellow}.811 & \cellcolor{lightyellow}.229 & 16.0 & & & & & & & & \\ 27 | CodecGS & 7.9 & 23.63 & .841 & .192 & 7.8 & 27.30 & .810 & .236 & \cellcolor{lightorange}10.3 & 29.81 & .906 & .251 & 9.0 & & & & \\ 28 | ContextGS_highrate & 8.3 & 24.29 & \cellcolor{lightyellow}.855 & \cellcolor{lightorange}.176 & 12.4 & 27.75 & \cellcolor{lightyellow}.811 & .231 & 19.3 & \cellcolor{lightred}30.41 & \cellcolor{lightyellow}.909 & .259 & 6.9 & & & & \\ 29 | Compact3D 32K & 13.0 & 23.44 & .838 & .198 & 13.0 & 27.12 & .806 & .240 & 19.0 & 29.90 & .907 & .251 & 13.0 & & & & \\ 30 | Compact3D 16K & 13.6 & 23.39 & .836 & .200 & 12.0 & 27.03 & .804 & .243 & 18.0 & 29.90 & .906 & .252 & 12.0 & & & & \\ 31 | CompGS & 13.7 & 23.70 & .837 & .208 & 10.1 & 27.26 & .803 & .239 & 17.3 & 29.69 & .901 & .279 & 9.2 & & & & \\ 32 | RDO-Gaussian & 13.8 & 23.34 & .835 & .195 & 12.0 & 27.05 & .802 & .239 & 23.5 & 29.63 & .902 & .252 & 18.0 & 33.12 & \cellcolor{lightyellow}.967 & \cellcolor{lightyellow}.035 & 2.3 \\ 33 | Reduced3DGS & 14.0 & 23.57 & .840 & .188 & 14.0 & 27.10 & .809 & \cellcolor{lightorange}.226 & 29.0 & 29.63 & .902 & \cellcolor{lightyellow}.249 & 18.0 & & & & \\ 34 | SOG w/o SH & 14.7 & 23.15 & .828 & .198 & 9.3 & 26.56 & .791 & .241 & 16.7 & 29.12 & .892 & .270 & 5.7 & 31.37 & .959 & .043 & 2.0 \\ 35 | MesonGS c3 & 16.0 & 23.29 & .835 & .197 & 17.4 & 26.99 & .797 & .246 & 25.9 & 29.48 & .903 & .252 & 29.0 & 32.96 & \cellcolor{lightorange}.968 & \cellcolor{lightred}.033 & 3.5 \\ 36 | Compressed3D & 16.5 & 23.32 & .832 & .194 & 17.3 & 26.98 & .801 & .238 & 28.8 & 29.38 & .898 & .253 & 25.3 & 32.94 & \cellcolor{lightyellow}.967 & \cellcolor{lightred}.033 & 3.7 \\ 37 | MesonGS c1 & 16.7 & 23.31 & .835 & .196 & 18.5 & 26.99 & .796 & .247 & 28.5 & 29.50 & .903 & .251 & 31.1 & 32.94 & \cellcolor{lightorange}.968 & \cellcolor{lightred}.033 & 3.9 \\ 38 | SOG & 17.0 & 23.56 & .837 & .186 & 22.8 & 27.08 & .799 & .230 & 40.3 & 29.26 & .894 & .268 & 17.7 & 33.23 & .966 & \cellcolor{lightorange}.034 & 4.1 \\ 39 | Compact3DGS+PP & 17.6 & 23.32 & .831 & .202 & 20.9 & 27.03 & .797 & .247 & 29.1 & 29.73 & .900 & .258 & 23.8 & 32.88 & \cellcolor{lightorange}.968 & \cellcolor{lightorange}.034 & 2.8 \\ 40 | EAGLES & 18.8 & 23.37 & .84 & .20 & 29.0 & 27.23 & .81 & .24 & 54.0 & 29.86 & \cellcolor{lightorange}.91 & .25 & 52.0 & & & & \\ 41 | FCGS-highrate & 18.8 & 23.62 & .839 & .184 & 33.6 & 27.39 & .806 & \cellcolor{lightorange}.226 & 67.2 & 29.58 & .899 & \cellcolor{lightorange}.248 & 54.5 & & & & \\ 42 | Scaffold-GS & 19.0 & 23.96 & .853 & \cellcolor{lightyellow}.177 & 87.0 & 27.50 & .806 & .252 & 156.0 & 30.21 & .906 & .254 & 66.0 & & & & \\ 43 | Compact3DGS & 19.5 & 23.32 & .831 & .201 & 39.4 & 27.08 & .798 & .247 & 48.8 & 29.79 & .901 & .258 & 43.2 & 33.33 & \cellcolor{lightorange}.968 & \cellcolor{lightorange}.034 & 5.8 \\ 44 | \textbf{3DGS-30K} & 19.7 & 23.14 & .841 & .183 & 411.0 & 27.21 & \cellcolor{lightred}.815 & \cellcolor{lightred}.214 & 734.0 & 29.41 & .903 & \cellcolor{lightred}.243 & 676.0 & 33.31 & & & \\ 45 | LightGaussian & 19.8 & 23.11 & .817 & .231 & 22.0 & 27.28 & .805 & .243 & 42.0 & & & & & 32.72 & .965 & .037 & 7.8 \\ 46 | FCGS-lowrate & 20.1 & 23.48 & .833 & .193 & 18.8 & 27.05 & .798 & .237 & 36.3 & 29.27 & .893 & .257 & 30.1 & & & & \\ 47 | EAGLES-Small & 22.1 & 23.10 & .82 & .22 & 19.0 & 26.94 & .80 & .25 & 47.0 & 29.92 & .90 & .25 & 33.0 & & & & \\ 48 | \bottomrule 49 | \end{tabular} 50 | \end{adjustbox} 51 | \newline\newline 52 | \noindent Note: The best methods in each category are highlighted (\colorbox{lightred}{fist}, \colorbox{lightorange}{second}, \colorbox{lightyellow}{third}). The ranks represent the average rankings of the methods across all available datasets. The quality metrics PSNR, SSIM, and LPIPS are equally weighted with the model size, meaning they each contribute one-sixth to the ranks, while the size contributes half. 53 | 54 | \end{minipage} 55 | } -------------------------------------------------------------------------------- /data_extraction/latex/3dgs_table_densification.tex: -------------------------------------------------------------------------------- 1 | \rotatebox{90}{ 2 | \begin{minipage}{\textheight} % Adjust the minipage width to fit the content 3 | \section*{Survey Table} 4 | % Have a look into ../tex_templates/preamble.tex for packages and color definitions 5 | 6 | % color every second line in grey 7 | \rowcolors{2}{gray!25}{white} 8 | 9 | \footnotesize 10 | \setlength{\tabcolsep}{3pt} 11 | 12 | % center the table 13 | \begin{adjustbox}{center} 14 | \begin{tabular}{ll|lrrr|lrrr|lrrr} 15 | \toprule 16 | Method & Rank & \multicolumn{4}{c|}{Tanks and Temples} & \multicolumn{4}{c|}{Mip-NeRF 360} & \multicolumn{4}{c|}{Deep Blending} \\ 17 | & \tiny & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny k Gauss & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny k Gauss & \tiny PSNR$\uparrow$ & \tiny SSIM$\uparrow$ & \tiny LPIPS$\downarrow$ & \tiny k Gauss \\ 18 | \midrule 19 | Octree-GS & \cellcolor{lightred}3.1 & \cellcolor{lightred}24.68 & \cellcolor{lightred}.866 & \cellcolor{lightorange}.153 & 443 & \cellcolor{lightred}28.05 & \cellcolor{lightyellow}.819 & .217 & 657 & \cellcolor{lightred}30.49 & \cellcolor{lightorange}.912 & .241 & \cellcolor{lightred}112 \\ 20 | GaussianSpa & \cellcolor{lightorange}3.6 & 24.00 & .850 & .172 & \cellcolor{lightyellow}322 & \cellcolor{lightorange}27.83 & .682 & .214 & \cellcolor{lightorange}558 & \cellcolor{lightyellow}30.13 & \cellcolor{lightred}.913 & .236 & 527 \\ 21 | Mini-Splatting & \cellcolor{lightyellow}3.7 & 23.18 & .835 & .202 & \cellcolor{lightred}200 & 27.34 & \cellcolor{lightorange}.822 & .217 & \cellcolor{lightred}490 & 29.98 & \cellcolor{lightyellow}.908 & .253 & \cellcolor{lightyellow}350 \\ 22 | Taming3DGS & 5.0 & 23.89 & .835 & .207 & \cellcolor{lightorange}290 & 27.29 & .799 & .253 & \cellcolor{lightyellow}630 & 27.79 & .822 & .263 & \cellcolor{lightorange}270 \\ 23 | Taming3DGS (Big) & 5.2 & \cellcolor{lightyellow}24.04 & .851 & .170 & 1,840 & \cellcolor{lightyellow}27.79 & \cellcolor{lightorange}.822 & \cellcolor{lightorange}.205 & 3,310 & \cellcolor{lightorange}30.14 & .907 & \cellcolor{lightyellow}.235 & 2,810 \\ 24 | GaussianPro & 5.4 & \cellcolor{lightorange}24.09 & \cellcolor{lightorange}.862 & .185 & 1,441 & 27.43 & .813 & .219 & 3,403 & 29.79 & \cellcolor{lightred}.913 & \cellcolor{lightorange}.222 & 2,582 \\ 25 | AtomGS & 5.7 & 23.70 & .849 & \cellcolor{lightyellow}.166 & 1,480 & 27.38 & .816 & \cellcolor{lightyellow}.211 & 3,140 & & & & \\ 26 | Mini-Splatting-D & 5.9 & 23.23 & \cellcolor{lightyellow}.853 & \cellcolor{lightred}.140 & 4,280 & 27.51 & \cellcolor{lightred}.831 & \cellcolor{lightred}.176 & 4,690 & 29.88 & .906 & \cellcolor{lightred}.211 & 4,630 \\ 27 | Color-cued GS & 6.1 & 23.18 & .830 & .198 & 370 & 27.07 & .797 & .249 & 646 & 29.71 & .902 & .255 & 644 \\ 28 | \bottomrule 29 | \end{tabular} 30 | \end{adjustbox} 31 | \newline\newline 32 | \noindent Note: The best methods in each category are highlighted (\colorbox{lightred}{fist}, \colorbox{lightorange}{second}, \colorbox{lightyellow}{third}). The ranks represent the average rankings of the methods across all available datasets. The quality metrics PSNR, SSIM, and LPIPS are equally weighted with the model size, meaning they each contribute one-sixth to the ranks, while the size contributes half. 33 | 34 | \end{minipage} 35 | } -------------------------------------------------------------------------------- /data_extraction/latex/datasets_and_evaluation_statistics.tex: -------------------------------------------------------------------------------- 1 | \section{Datasets and Evaluation Statistics} 2 | 3 | \subsection{Datasets} 4 | % % describe all datasets briefly 5 | Performance and quality assessment of 3D Gaussian Splatting algorithms is typically performed on multiple datasets. These datasets provide 3D scenes or objects with various properties, such as varying levels of detail, lighting conditions, and complexities, which allow for comprehensive evaluation of the algorithms. \\ 6 | In our survey we include Tanks and Temples\cite{TanksAndTemples}, MipNerf360\cite{MipNeRF360}, Deep Blending\cite{DeepBlending} as real-world datasets, and Synthetic NeRF\cite{SyntheticNeRF} as a synthetic dataset. From Tanks and Temples we include ``truck'' and ``train'' two unbounded outdoor scenes wich have a centered view point. The MipNerf360 dataset also has a centered view point but includes in- and outdoor scenes. The following scenes are included: ``bicycle'', ``bonsai'', ``counter'', ``flowers'', ``garden'', ``kitchen'', ``room'', ``stump'', ``treehill''. From the Deep Blending dataset we include ``Dr Johnson'' and ``Palyroom'' two indoor scenes with a viewpoint directed outward. The synthetic scenes: chair, drums, ficus, hotdog, lego, material, mic, ship stem from the SyntheticNeRF dataset. 7 | 8 | \subsection{Evaluation Statistics} 9 | % !!! check gsplat-protocol / check downsampling factor in 3DGS; maybe in code. 10 | % % describe eval statistics used in the table -------------------------------------------------------------------------------- /data_extraction/latex/survey.bib: -------------------------------------------------------------------------------- 1 | % NeRF paper 2 | @inproceedings{mildenhall2020nerf, 3 | title={NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis}, 4 | author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng}, 5 | year={2020}, 6 | booktitle={ECCV}, 7 | } 8 | % NeRF compression papers 9 | @inproceedings{li2023compressing, 10 | title={Compressing volumetric radiance fields to 1 mb}, 11 | author={Li, Lingzhi and Shen, Zhen and Wang, Zhongshu and Shen, Li and Bo, Liefeng}, 12 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 13 | pages={4222--4231}, 14 | year={2023} 15 | } 16 | @inproceedings{chen2024far, 17 | title={How Far Can We Compress Instant-NGP-Based NeRF?}, 18 | author={Chen, Yihang and Wu, Qianyi and Harandi, Mehrtash and Cai, Jianfei}, 19 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 20 | pages={20321--20330}, 21 | year={2024} 22 | } 23 | % general overview over 3DGS methods 24 | @article{wu2024recent, 25 | title={Recent advances in 3d gaussian splatting}, 26 | author={Wu, Tong and Yuan, Yu-Jie and Zhang, Ling-Xiao and Yang, Jie and Cao, Yan-Pei and Yan, Ling-Qi and Gao, Lin}, 27 | journal={Computational Visual Media}, 28 | pages={1--30}, 29 | year={2024}, 30 | publisher={Springer} 31 | } 32 | @article{fei20243d, 33 | title={3d gaussian splatting as new era: A survey}, 34 | author={Fei, Ben and Xu, Jingyi and Zhang, Rui and Zhou, Qingyuan and Yang, Weidong and He, Ying}, 35 | journal={IEEE Transactions on Visualization and Computer Graphics}, 36 | year={2024}, 37 | publisher={IEEE} 38 | } 39 | % is this already published in IEEE? 40 | @misc{chen2024survey3dgaussiansplatting, 41 | title={A Survey on 3D Gaussian Splatting}, 42 | author={Guikun Chen and Wenguan Wang}, 43 | year={2024}, 44 | eprint={2401.03890}, 45 | archivePrefix={arXiv}, 46 | primaryClass={cs.CV}, 47 | url={https://arxiv.org/abs/2401.03890}, 48 | } -------------------------------------------------------------------------------- /data_extraction/latex/tex_templates/contributions.tex: -------------------------------------------------------------------------------- 1 | In the following sections, we present a comprehensive overview of state-of-the-art 3D Gaussian Splatting (3DGS) compression methods. 2 | Although each method shares the common goal of minimizing memory usage while preserving rendering quality and speed, their strategies differ significantly. 3 | The summaries aim to distill the key concepts behind each approach, enabling better comparison and serving as a valuable reference for future research in the field of 3DGS compression. -------------------------------------------------------------------------------- /data_extraction/latex/tex_templates/entry.tex: -------------------------------------------------------------------------------- 1 | \subsection{} 2 | 3 | \cite{} 4 | 5 |
-------------------------------------------------------------------------------- /data_extraction/latex/tex_templates/figure.tex: -------------------------------------------------------------------------------- 1 | \begin{figure}[H] 2 | \centering 3 | \includegraphics[width=\textwidth]{} 4 | \end{figure} -------------------------------------------------------------------------------- /data_extraction/latex/tex_templates/table.tex: -------------------------------------------------------------------------------- 1 | \rotatebox{90}{ 2 | \begin{minipage}{\textheight} % Adjust the minipage width to fit the content 3 | \section*{Survey Table} 4 | % Have a look into ../tex_templates/preamble.tex for packages and color definitions 5 | 6 | % color every second line in grey 7 | \rowcolors{2}{gray!25}{white} 8 | 9 | \footnotesize 10 | \setlength{\tabcolsep}{3pt} 11 | 12 | % center the table 13 | \begin{adjustbox}{center} 14 | 15 | \end{adjustbox} 16 | \newline\newline 17 | \noindent Note: The best methods in each category are highlighted (\colorbox{lightred}{fist}, \colorbox{lightorange}{second}, \colorbox{lightyellow}{third}). The ranks represent the average rankings of the methods across all available datasets. The quality metrics PSNR, SSIM, and LPIPS are equally weighted with the model size, meaning they each contribute one-sixth to the ranks, while the size contributes half. 18 | 19 | \end{minipage} 20 | } -------------------------------------------------------------------------------- /data_extraction/preprocess_images.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import os 3 | 4 | # Define the folder path 5 | folder_path = "project-page/static/images" 6 | 7 | # Target display height 8 | display_height = 250 9 | 10 | # Multiplier for high-resolution images (e.g., 2 for Retina displays) 11 | multiplier = 2 12 | 13 | # High-resolution height 14 | high_res_height = display_height * multiplier 15 | 16 | # Process each image in the folder 17 | for filename in os.listdir(folder_path): 18 | if filename.lower().endswith((".png", ".jpg", ".jpeg")): 19 | if "_h250" in filename or "_h500" in filename: 20 | continue 21 | 22 | file_path = os.path.join(folder_path, filename) 23 | 24 | # Open the image 25 | img = Image.open(file_path) 26 | # Get the original size 27 | original_width, original_height = img.size 28 | 29 | # Resize to 250px height (1x) 30 | new_width_1x = int((display_height / original_height) * original_width) 31 | img_1x = img.resize((new_width_1x, display_height), Image.LANCZOS) 32 | 33 | # Save the 1x version as WebP with height in the filename 34 | base, _ = os.path.splitext(filename) 35 | img_1x.save( 36 | os.path.join(folder_path, f"{base}_h{display_height}px.webp"), 37 | format="webp", 38 | quality=80, 39 | ) 40 | print( 41 | f"Resized and saved {base}_{display_height}px.webp to {new_width_1x}x{display_height}" 42 | ) 43 | 44 | # Resize to 500px height (2x for high-DPI displays) 45 | new_width_2x = int((high_res_height / original_height) * original_width) 46 | img_2x = img.resize((new_width_2x, high_res_height), Image.LANCZOS) 47 | 48 | # Save the 2x version as WebP with height in the filename 49 | img_2x.save( 50 | os.path.join(folder_path, f"{base}_h{high_res_height}px.webp"), 51 | format="webp", 52 | quality=80, 53 | ) 54 | print( 55 | f"Resized and saved {base}_{high_res_height}px@2x.webp to {new_width_2x}x{high_res_height}" 56 | ) 57 | 58 | # Save the original image as a PDF 59 | pdf_path = os.path.join(folder_path, f"{base}.pdf") 60 | img.save(pdf_path, "PDF", resolution=100.0) 61 | print(f"Saved {base}.pdf at full resolution") 62 | print("Processing complete!") 63 | -------------------------------------------------------------------------------- /data_extraction/vis/README.md: -------------------------------------------------------------------------------- 1 | This folder contains scripts to create visualizations for the survey paper and for talks. 2 | 3 | treemap_plot_ply.py creates treemap_plot.png and treemap_plot.pdf. 4 | 5 | heatmap_hist_plot.py creates all_cols_corr.pdf, most_cols_hist.pdf, opacity_scale_hist.pdf, relevant_cols_corr.pdf 6 | 7 | To run the scripts: 8 | - Install uv (https://docs.astral.sh/uv/getting-started/installation/). 9 | - `./treemap_plot_ply.py` (no arguments, Truck size hardcoded) 10 | - `./heatmap_hist_plot.py --input-file PATH_TO_PLY_FILE` 11 | 12 | 13 | -------------------------------------------------------------------------------- /data_extraction/vis/all_cols_corr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/all_cols_corr.pdf -------------------------------------------------------------------------------- /data_extraction/vis/heatmap_hist_plot.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env -S uv run --script 2 | # /// script 3 | # requires-python = ">=3.12" 4 | # dependencies = [ 5 | # "click", 6 | # "matplotlib", 7 | # "natsort", 8 | # "numpy", 9 | # "pandas", 10 | # "plyfile", 11 | # "seaborn", 12 | # ] 13 | # /// 14 | 15 | """ 16 | This script generates visualizations from a 3DGS .ply file: 17 | - Correlation heatmaps showing relationships between various columns 18 | - Histograms displaying the distribution of different attributes 19 | 20 | These graphs are used in the paper version of "3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods". 21 | 22 | Usage: 23 | - `./heatmap_hist_plot.py --input-file PATH_TO_PLY_FILE` 24 | """ 25 | 26 | from plyfile import PlyData 27 | import matplotlib.pyplot as plt 28 | import numpy as np 29 | import pandas as pd 30 | 31 | import click 32 | import matplotlib 33 | import natsort 34 | 35 | matplotlib.rcParams['pdf.fonttype'] = 42 36 | matplotlib.rcParams['ps.fonttype'] = 42 37 | 38 | import seaborn as sns 39 | sns.set_theme(style="whitegrid") 40 | 41 | 42 | def load_ply_data(input_file): 43 | """Load PLY data and convert to pandas DataFrame.""" 44 | print(f"Loading data from {input_file}") 45 | data = PlyData.read(input_file) 46 | vertex_data = data["vertex"] 47 | return pd.DataFrame(vertex_data.data) 48 | 49 | 50 | def create_relevant_cols_corr(df): 51 | """Create correlation heatmap for relevant columns.""" 52 | non_normal_cols = [ 53 | "x", "y", "z", "scale_0", "scale_1", "scale_2", 54 | "rot_0", "rot_1", "rot_2", "rot_3", "opacity", 55 | "f_dc_0", "f_dc_1", "f_dc_2", 56 | ] 57 | 58 | dfnn = df[non_normal_cols] 59 | corr_nn = dfnn.corr() 60 | 61 | plt.figure() 62 | sns.heatmap( 63 | corr_nn, 64 | xticklabels=corr_nn.columns.values, 65 | yticklabels=corr_nn.columns.values, 66 | vmin=-1.0, 67 | vmax=1.0, 68 | cmap="vlag", 69 | ) 70 | plt.gcf().set_size_inches(6, 6) 71 | plt.subplots_adjust(hspace=None) 72 | plt.subplots_adjust(left=0, right=1, top=1, bottom=0) 73 | plt.savefig("relevant_cols_corr.pdf", bbox_inches="tight", pad_inches=0) 74 | plt.close() 75 | 76 | 77 | def create_opacity_scale_hist(df): 78 | """Create histogram for opacity and scale columns.""" 79 | text_hist_cols = ["opacity", "scale_0"] 80 | 81 | dfins = df[text_hist_cols].hist(bins=50, layout=(1, 2), figsize=(6, 4), sharey=True) 82 | # Modify y-axis to show the amount in thousands ("k") 83 | for ax in dfins.flatten(): 84 | ax.yaxis.set_major_formatter( 85 | plt.FuncFormatter(lambda x, loc: "{:,}k".format(int(x / 1000))) 86 | ) 87 | plt.margins(0) 88 | plt.subplots_adjust(left=0, right=1, top=1, bottom=0) 89 | # plt.subplots_adjust(wspace=0.5) 90 | # plt.gcf().set_size_inches(8, 4) 91 | plt.savefig("opacity_scale_hist.pdf", bbox_inches="tight", pad_inches=0) 92 | plt.close() 93 | 94 | 95 | def create_most_cols_hist(df): 96 | """Create histograms for most columns.""" 97 | appendix_hist_cols = [ 98 | "x", "y", "z", "scale_0", "scale_1", "scale_2", 99 | "rot_0", "rot_1", "rot_2", "rot_3", "opacity", 100 | "f_dc_0", "f_dc_1", "f_dc_2", 101 | "f_rest_0", "f_rest_1", "f_rest_2", "f_rest_3", "f_rest_4", 102 | "f_rest_5", "f_rest_6", "f_rest_7", "f_rest_8", "f_rest_9", 103 | ] 104 | 105 | dfins = df[appendix_hist_cols].hist( 106 | bins=50, layout=(8, 3), figsize=(12, 18), sharey=True 107 | ) 108 | # Modify y-axis to show the amount in millions ("M") 109 | for ax in dfins.flatten(): 110 | ax.yaxis.set_major_formatter( 111 | plt.FuncFormatter(lambda x, loc: "{:,}M".format(int(x / 1_000_000))) 112 | ) 113 | plt.subplots_adjust(hspace=0.6) 114 | plt.margins(0) 115 | plt.subplots_adjust(left=0, right=1, top=1, bottom=0) 116 | plt.savefig("most_cols_hist.pdf", bbox_inches="tight", pad_inches=0) 117 | plt.close() 118 | 119 | 120 | def create_all_cols_corr(df): 121 | """Create correlation heatmap for all columns.""" 122 | non_normal_cols = [ 123 | "x", "y", "z", "scale_0", "scale_1", "scale_2", 124 | "rot_0", "rot_1", "rot_2", "rot_3", "opacity", 125 | "f_dc_0", "f_dc_1", "f_dc_2", 126 | ] 127 | 128 | sh_ac_cols = [f"f_rest_{i + j}" for i in range(15) for j in [0, 15, 30]] 129 | non_normal_cols.extend(sh_ac_cols) 130 | 131 | dfnn = df[non_normal_cols] 132 | corr_nn = dfnn.corr() 133 | 134 | plt.figure() 135 | sns.heatmap( 136 | corr_nn, 137 | xticklabels=corr_nn.columns.values, 138 | yticklabels=corr_nn.columns.values, 139 | vmin=-1.0, 140 | vmax=1.0, 141 | cmap="vlag", 142 | ) 143 | plt.subplots_adjust(left=0, right=1, top=1, bottom=0) 144 | plt.gcf().set_size_inches(16, 16) 145 | plt.savefig("all_cols_corr.pdf", bbox_inches="tight", pad_inches=0) 146 | plt.close() 147 | 148 | 149 | @click.command(help="Generate visualizations for 3D Gaussian Splatting point cloud data.") 150 | @click.option('--input-file', '-i', required=True, 151 | help='Path to the PLY file containing Gaussian Splatting point cloud data.') 152 | def main(input_file): 153 | """Main function to orchestrate the visualization creation process.""" 154 | df = load_ply_data(input_file) 155 | 156 | create_relevant_cols_corr(df) 157 | create_opacity_scale_hist(df) 158 | create_most_cols_hist(df) 159 | create_all_cols_corr(df) 160 | 161 | print("All visualizations have been successfully created.") 162 | 163 | 164 | if __name__ == "__main__": 165 | main() 166 | 167 | 168 | -------------------------------------------------------------------------------- /data_extraction/vis/most_cols_hist.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/most_cols_hist.pdf -------------------------------------------------------------------------------- /data_extraction/vis/opacity_scale_hist.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/opacity_scale_hist.pdf -------------------------------------------------------------------------------- /data_extraction/vis/relevant_cols_corr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/relevant_cols_corr.pdf -------------------------------------------------------------------------------- /data_extraction/vis/treemap_plot.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/treemap_plot.pdf -------------------------------------------------------------------------------- /data_extraction/vis/treemap_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/data_extraction/vis/treemap_plot.png -------------------------------------------------------------------------------- /datasets.bib: -------------------------------------------------------------------------------- 1 | @article{TanksAndTemples, 2 | author={Arno Knapitsch and Jaesik Park and Qian-Yi Zhou and Vladlen Koltun}, 3 | title={Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction}, 4 | journal={ACM Transactions on Graphics}, 5 | volume={36}, 6 | number={4}, 7 | year={2017}, 8 | url={https://www.tanksandtemples.org}, 9 | is_synthetic={False}, 10 | } 11 | 12 | @inproceedings{MipNeRF360, 13 | title={Mip-nerf 360: Unbounded anti-aliased neural radiance fields}, 14 | author={Barron, Jonathan T and Mildenhall, Ben and Verbin, Dor and Srinivasan, Pratul P and Hedman, Peter}, 15 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 16 | pages={5470--5479}, 17 | year={2022}, 18 | url={https://jonbarron.info/mipnerf360/}, 19 | is_synthetic={False}, 20 | } 21 | 22 | @article{SyntheticNeRF, 23 | title={Nerf: Representing scenes as neural radiance fields for view synthesis}, 24 | author={Mildenhall, Ben and Srinivasan, Pratul P and Tancik, Matthew and Barron, Jonathan T and Ramamoorthi, Ravi and Ng, Ren}, 25 | journal={Communications of the ACM}, 26 | volume={65}, 27 | number={1}, 28 | pages={99--106}, 29 | year={2021}, 30 | publisher={ACM New York, NY, USA}, 31 | url={https://www.matthewtancik.com/nerf}, 32 | is_synthetic={True}, 33 | } 34 | 35 | @article{DeepBlending, 36 | title={Deep blending for free-viewpoint image-based rendering}, 37 | author={Hedman, Peter and Philip, Julien and Price, True and Frahm, Jan-Michael and Drettakis, George and Brostow, Gabriel}, 38 | journal={ACM Transactions on Graphics (ToG)}, 39 | volume={37}, 40 | number={6}, 41 | pages={1--15}, 42 | year={2018}, 43 | publisher={ACM New York, NY, USA}, 44 | url={http://visual.cs.ucl.ac.uk/pubs/deepblending/}, 45 | is_synthetic={False}, 46 | } 47 | -------------------------------------------------------------------------------- /methods.bib: -------------------------------------------------------------------------------- 1 | @article{kerbl3Dgaussians, 2 | author = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George}, 3 | title = {3D Gaussian Splatting for Real-Time Radiance Field Rendering}, 4 | journal = {ACM Transactions on Graphics}, 5 | number = {4}, 6 | volume = {42}, 7 | month = {July}, 8 | year = {2023}, 9 | url = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/} 10 | } 11 | 12 | @misc{ye2023mathematical, 13 | title={Mathematical Supplement for the $\texttt{gsplat}$ Library}, 14 | author={Vickie Ye and Angjoo Kanazawa}, 15 | year={2023}, 16 | eprint={2312.02121}, 17 | archivePrefix={arXiv}, 18 | primaryClass={cs.MS}, 19 | url={https://docs.gsplat.studio/main/}, 20 | } -------------------------------------------------------------------------------- /methods/chen2024hac.md: -------------------------------------------------------------------------------- 1 | ### HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression 2 | 3 | The paper proposes a Hash-grid Assisted Context (HAC) framework for compressing 3D Gaussian Splatting (3DGS) models by leveraging the mutual information between attributes of unorganized 3D Gaussians (anchors) and hash grid features. Using Scaffold-GS as a base model, HAC queries the hash grid by anchor location to predict anchor attribute distributions for efficient entropy coding. The framework introduces an Adaptive Quantization Module (AQM) to dynamically adjust quantization step sizes. Furthermore, this method employs adaptive offset masking with learnable masks to eliminate invalid Gaussians and anchors, by leveraging the pruning strategy introduced by Compact3DGS and additionally removing anchors if all the attached offsets are pruned. 4 | -------------------------------------------------------------------------------- /methods/chen2025fcgs.md: -------------------------------------------------------------------------------- 1 | ### Fast Feedforward 3D Gaussian Splatting Compression 2 | 3 | -------------------------------------------------------------------------------- /methods/chen2025hac-plus.md: -------------------------------------------------------------------------------- 1 | ### HAC++: Towards 100X Compression of 3D Gaussian Splatting 2 | 3 | 4 | 5 | -------------------------------------------------------------------------------- /methods/cheng2024gaussianpro.md: -------------------------------------------------------------------------------- 1 | ### GaussianPro: 3D Gaussian Splatting with Progressive Propagation 2 | This method generates depth and normal maps that guide the growth and adjustment of Gaussians. It employs patch matching to propagate depth and normal information from neighboring pixels to generate new values. Geometric filtering and selection then identify pixels needing additional Gaussians, which are initialized using the propagated information. It also introduces a planar loss to ensure Gaussians match real surfaces more closely. This method enforces consistency between the Gaussian's rendered normal and the propagated normal using L1 and angular loss. 3 | -------------------------------------------------------------------------------- /methods/fan2024lightgaussian.md: -------------------------------------------------------------------------------- 1 | ### LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS 2 | 3 | LightGaussian aims to transform 3D Gaussians to a more efficient and compact form, avoiding the scalablity issues that arrises from the large number of SfM (Structure from Motion) points for unbounded scenes. Inspired by Network Pruning, the method identifies Gaussians that minimally contribute to scene reconstruction and employs a pruning and recovery process, thereby efficiently reducing redundancy in Gaussian counts while maintaining visual effects. Additionally, LightGaussian utilizes knowledge distillation and pseudo-view augmentation to transfer spherical harmonics efficients to a lower degree. Furthermore, the authors propose a Gaussian Vector Quantization based on the global significance of Gaussians to quantize all redundant attributes, achieving lower bitwidth representations with minimal accuracy losses. -------------------------------------------------------------------------------- /methods/fang2024minisplatting.md: -------------------------------------------------------------------------------- 1 | ### Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians 2 | Mini-Splatting enhances Gaussian distribution through Blur Split, which refines Gaussians in blurred regions, and Depth Reinitialization, which repositions Gaussians based on newly generated depth points, calculated from the mid-point of ray intersections with Gaussian ellipsoids, thus avoiding artifacts from alpha blending. For simplification, Intersection Preserving retains Gaussians with the greatest visual impact, while Sampling maintains geometric integrity and rendering quality, reducing complexity. 3 | -------------------------------------------------------------------------------- /methods/feng2024flashgs.md: -------------------------------------------------------------------------------- 1 | ### FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering 2 | The method reduces computational overhead by addressing inefficiencies in direct intersection calculations for elongated ellipses. It employs a two-stage filtering process: (1) a tightly tangent bounding box is used to set a coarse range, and (2) tiles intersecting the bounding box are checked for ellipse intersections. Additionally, FlashGS reduces global memory queries by employing a software pipelining approach that overlaps instruction dispatch, optimizing data retrieval and rendering efficiency. 3 | -------------------------------------------------------------------------------- /methods/girish2024eagles.md: -------------------------------------------------------------------------------- 1 | ### EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS 2 | 3 | 4 | The authors of this approach observed that in 3DGS, the color and rotation attributes account for over 80% of memory usage; thus, they propose compressing these attributes via a latent quantization framework. Additionally, they quantize the opacity coefficients of the Gaussians, improving optimization and resulting in fewer floaters or visual artifacts in novel view reconstructions. To reduce the number of redundant Gaussians resulting from frequent densification (via cloning and splitting), the approach employs a pruning stage to identify and remove Gaussians with minimal influence on the full reconstruction. For this, an influence metric is introduced, which considers both opacity and transmittance. -------------------------------------------------------------------------------- /methods/hu2024gsplat.md: -------------------------------------------------------------------------------- 1 | ### gsplat 2 | 3 | This approach leverages 3D Gaussian Splatting as Markov Chain Monte Carlo (3DGS-MCMC), interpreting the training process of positioning and optimizing Gaussians as a sampling procedure rather than minimizing a predefined loss function. 4 | 5 | Additionally, it incorporates compression techniques derived from the 6 | SOG paper, which organizes the parameters of 3DGS in a 2D grid, capitalizing on perceptual redundancies found in natural scenes, thereby significantly reducing storage requirements. 7 | 8 | Further compression is achieved by applying methods from 9 | Making Gaussian Splats more smaller, which reduces the size of Gaussian splats by clustering spherical harmonics into discrete elements and storing them as FP16 values. 10 | 11 | This technique is implemented in 12 | gsplat, an open-source library designed for CUDA-accelerated differentiable rasterization of 3D Gaussians, equipped with Python bindings. 13 | 14 | -------------------------------------------------------------------------------- /methods/jung2024relaxing: -------------------------------------------------------------------------------- 1 | ### RAIN-GS: Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting 2 | This method introduces three strategies to enhance 3D Gaussian splatting. First, it employs Sparse-Large-Variance (SLV) initialization, which begins with a small number of Gaussians to capture the overall structure of the point cloud. Second, progressive Gaussian low-pass filtering encourages initial large-scale Gaussians to cover wider areas. Third, the adaptive bound-expanding split (ABE-Split) algorithm moves Gaussians beyond their initial bounds, encouraging them to explore a broader space for more accurate geometry reconstruction. 3 | -------------------------------------------------------------------------------- /methods/kim2024CVPR.md: -------------------------------------------------------------------------------- 1 | ### Color-cued Efficient Densification Method for 3D Gaussian Splatting 2 | This method introduces a simple yet effective modification to the densification process in the original 3D Gaussian Splatting (3DGS). It leverages the view-independent (0th) spherical harmonics (SH) coefficient gradient to better assess color cues for densification, while using the 2D position gradient more coarsely to refine areas where structure-from-motion (SfM) struggles to capture fine structures. 3 | -------------------------------------------------------------------------------- /methods/lee2024compact.md: -------------------------------------------------------------------------------- 1 | ### Compact 3D Gaussian Representation for Radiance Field 2 | 3 | This approach introduces a Gaussian volume mask to prune non-essential Gaussians and a compact attribute representation for both view-dependent color and geometric attributes. The volume-based masking strategy combines opacity and scale to selectively remove redundant Gaussians. For color attribute compression, spatial redundancy is exploited by incorporating a grid-based (Instant-NGP) neural field, allowing efficient representation of view-dependent colors without storing attributes per Gaussian. Given the limited variation in scale and rotation, geometric attribute compression employs a compact codebook-based representation to identify and reuse similar geometries across the scene. Additionally, the authors propose quantization and entropy-coding as post-processing steps for further compression. -------------------------------------------------------------------------------- /methods/lee2025compression3dgaussiansplatting.md: -------------------------------------------------------------------------------- 1 | ### Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs 2 | 3 | This method~\cite{lee2025compression3dgaussiansplatting} introduces an effective approach for compressing 3D Gaussian Splatting by employing optimized feature planes and integrating them with standard video codecs. More specifically CodecGS introduces progressive tri-planes, where the tri-plane takes 3D point positions x as input and predicts the corresponding attributes for each point. A two-phase training, starting with standard 3DGS~\cite{kerbl3Dgaussians} training followed by feature plane training ensures densification is consistent. Progressive training addresses the instability of feature plane training due to sparse 3DGS signals. DCT entropy modeling is employed to transform the feature planes. After training, feature planes are normalized and converted to 16-bit integers, corresponding to the YUV 16-bit format, and are encoded by a standard video codec. -------------------------------------------------------------------------------- /methods/li2024mvgsplatting.md: -------------------------------------------------------------------------------- 1 | ### MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification 2 | This method introduces adaptive depth density control to enhance 2D GS. 2D GS renders depth maps. In this method, the render depth maps are dynamically divided into near, mid, and far regions based on Kernel Density Estimation (KDE) and Fast Fourier Transform (FFT), focusing densification in under-reconstructed near and far areas. Geometric consistency checks and depth projection ensure adaptive densification without over-reconstruction, particularly in these critical regions. 3 | -------------------------------------------------------------------------------- /methods/liu2024atomgs.md: -------------------------------------------------------------------------------- 1 | ### AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field 2 | This method prioritizes fine details through Atom Gaussians, which are isotropic and uniformly sized to align closely with the scene's geometry, while large Gaussians are merged to cover smooth surfaces. In addition, Geometry-Guided Optimization uses an Edge-Aware Normal Loss and multi-scale SSIM to maintain geometric accuracy. The Edge-Aware Normal Loss is calculated as the product of the normal map, derived from the pre-optimized 3DGS, and the edge map, which is derived from the gradient magnitude of the ground truth RGB image. 3 | 4 | -------------------------------------------------------------------------------- /methods/liu2024compgs.md: -------------------------------------------------------------------------------- 1 | ### CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting 2 | 3 | The paper CompGS proposes a hybrid primitive structure with anchor primitives to predict the attributes of coupled primitives, resulting in compact residual representations. A rate-constrained optimization scheme further enhances compactness by jointly minimizing both rendering distortion and bitrate. The bitrate of both anchor and coupled primitives ist modeled by entropy estimation. 4 | -------------------------------------------------------------------------------- /methods/liu2024hemgs.md: -------------------------------------------------------------------------------- 1 | ### HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression 2 | 3 | -------------------------------------------------------------------------------- /methods/lu2024scaffold.md: -------------------------------------------------------------------------------- 1 | ### Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering 2 | 3 | Scaffold-GS introduces anchor points that leverage scene structure to guide the distribution of local 3D Gaussians. Attributes like opacity, color, rotation, and scale are dynamically predicted for Gaussians linked to each anchor within the viewing frustum, enabling adaptation to different viewing directions and distances. Initial anchor points are derived by voxelizing the sparse, irregular point cloud from Structure from Motion (SfM), forming a regular grid. To refine and grow the anchors, Gaussians are spatially quantized using voxels, with new anchors created at the centers of significant voxels, identified by their average gradient over N training steps. Random elimination and opacity-based pruning regulate anchor growth and refinement. -------------------------------------------------------------------------------- /methods/morgenstern2024compact.md: -------------------------------------------------------------------------------- 1 | ### Compact 3D Scene Representation via Self-Organizing Gaussian Grids 2 | 3 | Compressing 3D data is challenging, but many effective solutions exist for compressing 2D data (such as images). The authors propose a new method to organize 3DGS parameters into a 2D grid, drastically reducing storage requirements without compromising visual quality. This organization exploits perceptual redundancies in natural scenes. They introduce a highly parallel sorting algorithm, PLAS, which arranges Gaussian parameters into a 2D grid, maintaining local neighborhood structure and ensuring smoothness. This solution is particularly innovative because no existing method efficiently handles a 2D grid with millions of points. During training, a smoothness loss is applied to enforce local smoothness in the 2D grid, enhancing the compressibility of the data. 4 | The key insight is that smoothness needs to be enforced during training to enable efficient compression. -------------------------------------------------------------------------------- /methods/navaneet2023compact3d.md: -------------------------------------------------------------------------------- 1 | ### Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization 2 | 3 | This approach introduces a vector quantization method based on the K-means algorithm to quantize the Gaussian parameters in 3D Gaussian splatting, as many Gaussians may share similar parameters. Only a small codebook is stored along with the index of the code for each Gaussian, resulting in a large reduction in the storage of the learned radiance fields and a reduction of the memory footprint at rendering time. Additionally, the indices are further compressed by sorting the Gaussians based on one of the quantized parameters and storing the indices using a method similar to Run-Length-Encoding (RLE). To reduce the number of Gaussians, this method applies a regularizer to encourage zero opacity, before pruning Gaussians with opacity smaller than a threshold. -------------------------------------------------------------------------------- /methods/niedermayr2024compressed.md: -------------------------------------------------------------------------------- 1 | ### Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis 2 | 3 | The authors propose a compressed 3D Gaussian splat representation consisting of three main steps: 1. sensitivity-aware clustering, where scene parameters are measured according to their contribution to the training images and encoded into compact codebooks via sensitivity-aware vector quantization; 2. quantization-aware fine-tuning, which recovers lost information by fine-tuning parameters at reduced bit-rates using quantization-aware training; and 3. entropy encoding, which exploits spatial coherence through entropy and run-length encoding by linearizing 3D Gaussians along a space-filling curve. 4 | Furthermore, a renderer for the compressed scenes utilizing GPU-based sorting and rasterization is proposed, enabling real-time novel view synthesis on low-end devices. -------------------------------------------------------------------------------- /methods/papantonakis2024reducing.md: -------------------------------------------------------------------------------- 1 | ### Reducing the Memory Footprint of 3D Gaussian Splatting 2 | 3 | This approach addresses three main issues contributing to large storage sizes in 3D Gaussian Splatting (3DGS). To reduce the number of 3D Gaussian primitives, the authors introduce a scale- and resolution-aware redundant primitive removal method. This extends opacity-based pruning by incorporating a redundancy score to identify regions with many low-impact primitives. To mitigate storage size due to spherical harmonic coefficients, they propose adaptive adjustment of spherical harmonic (SH) bands. This involves evaluating color consistency across views and reducing higher-order SH bands when view-dependent effects are minimal. Additionally, recognizing the limited need for high dynamic range and precision for most primitive attributes, they develop a codebook using K-means clustering and apply 16-bit half-float quantization to the remaining uncompressed floating point values. -------------------------------------------------------------------------------- /methods/pateux2025bogauss.md: -------------------------------------------------------------------------------- 1 | ### BOGausS: Better Optimized Gaussian Splatting 2 | BOGausS proposes an improved training strategy with the following contributions: 3 |
    4 |
  • Confidence-aware updates: Bayesian confidence sets a minimum Gaussian size and scales gradients by distance, preventing oversampling and accelerating convergence.
    • 5 |
  • Unbiased Sparse Adam: Reformulated optimizer yields unbiased moment estimates and inherits state after splits for smoother training.
    • 6 |
  • Density-preserving splits: Splits along the longest axis, adjusting size and opacity to keep total density constant and the loss curve smooth.
    • 7 |
  • Rate-distortion densification: Prioritises splits by reconstruction error and prunes by effective opacity for compact, high-quality models.
    • 8 |
  • Optional exposure correction refines appearance under photometric inconsistencies.
    • 9 |
-------------------------------------------------------------------------------- /methods/ren2024octreegs.md: -------------------------------------------------------------------------------- 1 | ### Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians 2 | Octree-GS introduces an octree structure to 3D Gaussian splatting. Starting with a sparse point cloud, an octree is constructed for the bounded 3D space, where each level corresponds to a set of anchor Gaussians assigned to different levels of detail (LOD). This method selects the necessary LOD based on the observation view, gradually accumulating Gaussians from higher LODs for final rendering. The model is trained using standard image reconstruction and volume regularization losses. 3 | -------------------------------------------------------------------------------- /methods/seo2024flod.md: -------------------------------------------------------------------------------- 1 | ### FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering 2 | This method introduces a multi-level training approach to customize and reduce the size of 3D Gaussian splatting. Starting from Structure from Motion (SfM) points, training is performed level by level. At each level, (a) a scale constraint is applied to control Gaussian size, and (b) overlap pruning is used to reduce excessive Gaussian overlap. The final model supports maximum-level rendering for high-quality output or selective multi-level rendering for more efficient performance. 3 | -------------------------------------------------------------------------------- /methods/sun2024f3dgs.md: -------------------------------------------------------------------------------- 1 | ### F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting 2 | 3 | 4 | The paper introduces a novel 3D Gaussian compression method using structured coordinates and decomposed representations through factorization. Inspired by tensor or matrix factorization techniques, this method generates 3D coordinates via a tensor product of 1D or 2D coordinates, enhancing spatial efficiency. It extends factorization to include attributes like color, scale, rotation, and opacity, which compresses the model size while maintaining essential characteristics. A binary mask is employed to eliminate non-essential Gaussians, significantly accelerating training and rendering speeds. 5 |

6 | Note: This paper is currently not included in the survey table because it shows unusually high results in the Tanks and Temples dataset and reports higher results for the original 3DGS than those in the original publication. This raises the possibility that their testing methods may differ from those used in other papers. -------------------------------------------------------------------------------- /methods/taming20243dgs.md: -------------------------------------------------------------------------------- 1 | ### Taming 3DGS: High-Quality Radiance Fields with Limited Resources 2 | 3 | This method employs a global scoring approach to guide the addition of Gaussians, ensuring efficient densification. Each Gaussian is assigned a score based on four factors: 1) gradient, 2) pixel coverage, 3) per-view saliency, and 4) core attributes like opacity, depth, and scale. Gaussians with the top B scores, where B is the desired number of new Gaussians, are then split or cloned to optimize the scene's representation. By calculating a composite score that reflects both the scene’s structural complexity and visual importance, only the most critical areas are targeted for Gaussian splitting or cloning, resulting in more effective scene representation. 4 | -------------------------------------------------------------------------------- /methods/wang2024contextgs.md: -------------------------------------------------------------------------------- 1 | ### ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model 2 | 3 | This paper proposes the first autoregressive model at the anchor level for 3DGS compression. This work divides anchors into different levels and the anchors that are not coded yet can be predicted based on the already coded ones in all the coarser levels, leading to more accurate modeling and higher coding efficiency. To further improve the efficiency of entropy coding, a low-dimensional quantized feature is introduced as the hyperprior for each anchor, which can be effectively compressed. This work can be applied to both Scaffold-GS and vanilla 3DGS. 4 | -------------------------------------------------------------------------------- /methods/wang2024end.md: -------------------------------------------------------------------------------- 1 | ### End-to-End Rate-Distortion Optimized 3D Gaussian Representation 2 | 3 | This paper introduces RDO-Gaussian, an end-to-end Rate-Distortion Optimized 3D Gaussian representation. The authors achieve flexible, continuous rate control by formulating 3D Gaussian representation learning as a joint optimization of rate and distortion. Rate-distortion optimization is realized through dynamic pruning and entropy-constrained vector quantization (ECVQ). Gaussian pruning involves learning a mask to eliminate redundant Gaussians and adaptive SHs pruning assigns varying SH degrees to each Gaussian based on material and illumination needs. The covariance and color attributes are discretized through ECVQ, which performs vector quantization. -------------------------------------------------------------------------------- /methods/wu2024implicit.md: -------------------------------------------------------------------------------- 1 | ### Implicit Gaussian Splatting with Efficient Multi-Level Tri-Plane Representation 2 | 3 | This method introduces a hybrid representation for splatting-based radiance fields, where Gaussian primitives are separated into explicit point cloud and implicit attribute features. The attribute features are encoded using a multi-resolution multi-level tri-plane architecture integrated with a residual-based rendering pipeline. It employs a level-based progressive training scheme for joint optimization of point clouds and tri-planes, starting with coarse attributes and refining them with higher-level details. Spatial regularization and a bootstrapping scheme are applied to enhance the consistency and stability of the Gaussian attributes during training. -------------------------------------------------------------------------------- /methods/xie2024mesongs.md: -------------------------------------------------------------------------------- 1 | ### MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation 2 | 3 | MesonGS employs universal Gaussian pruning by evaluating the importance of Gaussians through forward propagation, considering both view-dependent and view-independent features. It transforms rotation quaternions into Euler angles to reduce storage requirements and applies region adaptive hierarchical transform (RAHT) to reduce entropy in key attributes. Block quantization is performed on attribute channels by dividing them into multiple blocks and perform quantization for each block individually, using vector quantization for compressing less important attributes. Geometry is compressed using an octree, and all elements are packed with the LZ77 codec. A finetune scheme is implemented post-training to restore quality. -------------------------------------------------------------------------------- /methods/yan2024multiscale.md: -------------------------------------------------------------------------------- 1 | ### Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering 2 | This method aggregates small Gaussians below a size threshold during early training, enlarging and inserting them into the scene at various resolution scales. These multi-scale Gaussians are then selected for rendering based on their "pixel coverage" at the current resolution. During rendering, only Gaussians with pixel coverage between adaptively selected minimum and maximum are selected. These pixel-coverage values are dynamically updated based on the aggregation results from the first stage. 3 | -------------------------------------------------------------------------------- /methods/yang2024spectrally.md: -------------------------------------------------------------------------------- 1 | ### SUNDAE: Spectrally Pruned Gaussian Fields with Neural Compensation 2 | This method combines graph-based pruning of Gaussian primitives with a convolutional neural network to retain high-frequency details. First, it "warms up" the 3D Gaussian field before applying a graph-based pruning strategy. The pruning step uses a graph built on the spatial relationships between the Gaussians and applies a band-limited graph filter to selectively down-sample, preserving important features. A convolutional neural network is then used to compensate for any losses caused by the pruning, ensuring that both high-frequency details and general low-frequency features are retained. 3 | -------------------------------------------------------------------------------- /methods/zhang2024fregs.md: -------------------------------------------------------------------------------- 1 | ### FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization 2 | This approach is designed to address over-reconstruction from a frequency-based perspective. It minimizes the discrepancy between the frequency spectra of rendered images and the corresponding ground truth. This method explicitly regularizes amplitude and phase differences within Fourier space to achieve more accurate results. 3 | -------------------------------------------------------------------------------- /methods/zhang2024gaussianspa.md: -------------------------------------------------------------------------------- 1 | ### GaussianSpa: An “Optimizing-Sparsifying” Simplification Framework for Compact and High-Quality 3D Gaussian Splatting 2 | 3 | GaussianSpa, an optimization-based simplification framework that significantly reduces the number of Gaussians while maintaining high rendering quality. GaussianSpa formulates simplification as an optimization problem and introduces an “optimizing-sparsifying” solution that alternately solves two independent sub-problems, gradually imposing strong sparsity onto the Gaussians in the training process. -------------------------------------------------------------------------------- /methods/zhang2024pixelgs.md: -------------------------------------------------------------------------------- 1 | ### Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting 2 | The Pixel-GS method introduces a pixel-aware gradient to address under-reconstruction and needle-like artifacts in 3DGS. This gradient guides densification when the averaged scale gradient across multiple views exceeds a threshold, unlike the original method that only considers a single view. Additionally, Pixel-GS proposes a depth-dependent gradient scaling strategy to balance the influence of Gaussians on affected areas, as near-view Gaussians often receive an excessive number of gradients due to affecting too many pixels. 3 | -------------------------------------------------------------------------------- /methods_densification.bib: -------------------------------------------------------------------------------- 1 | @misc{pateux2025bogauss, 2 | author={Pateux, Stéphane and Gendrin, Matthieu and Morin, Luce and Ladune, Théo and Jiang, Xiaoran}, 3 | title={BOGausS: Better Optimized Gaussian Splatting}, 4 | year={2025}, 5 | eprint={2504.01844}, 6 | archivePrefix={arXiv}, 7 | primaryClass={cs.CV}, 8 | url={https://arxiv.org/abs/2504.01844}, 9 | shortname = {BOGausS} 10 | } 11 | 12 | @inproceedings{taming20243dgs, 13 | author={Saswat Subhajyoti Mallick and Rahul Goel and Bernhard Kerbl and Francisco Vicente Carrasco and Markus Steinberger and Fernando De La Torre}, 14 | title={Taming 3DGS: High-Quality Radiance Fields with Limited Resources}, 15 | booktitle = {SIGGRAPH Asia 2024 Conference Papers}, 16 | year={2024}, 17 | doi = {10.1145/3680528.3687694}, 18 | url = {https://humansensinglab.github.io/taming-3dgs/}, 19 | shortname = {Taming3DGS} 20 | } 21 | 22 | @InProceedings{kim2024CVPR, 23 | author = {Kim, Sieun and Lee, Kyungjin and Lee, Youngki}, 24 | title = {Color-cued Efficient Densification Method for 3D Gaussian Splatting}, 25 | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, 26 | month = {June}, 27 | year = {2024}, 28 | pages = {775-783}, 29 | url={https://openaccess.thecvf.com/content/CVPR2024W/3DMV/html/Kim_Color-cued_Efficient_Densification_Method_for_3D_Gaussian_Splatting_CVPRW_2024_paper.html}, 30 | shortname = {Color-cued GS} 31 | } 32 | @misc{fang2024minisplatting, 33 | title={Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians}, 34 | author={Guangchi Fang and Bing Wang}, 35 | year={2024}, 36 | eprint={2403.14166}, 37 | archivePrefix={arXiv}, 38 | primaryClass={cs.CV}, 39 | url={https://arxiv.org/abs/2403.14166}, 40 | shortname = {Mini-Splatting} 41 | } 42 | @misc{cheng2024gaussianpro, 43 | title={GaussianPro: 3D Gaussian Splatting with Progressive Propagation}, 44 | author={Kai Cheng and Xiaoxiao Long and Kaizhi Yang and Yao Yao and Wei Yin and Yuexin Ma and Wenping Wang and Xuejin Chen}, 45 | year={2024}, 46 | eprint={2402.14650}, 47 | archivePrefix={arXiv}, 48 | primaryClass={cs.CV}, 49 | url={https://kcheng1021.github.io/gaussianpro.github.io/}, 50 | shortname = {GaussianPro} 51 | } 52 | @misc{zhang2024fregs, 53 | title={FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization}, 54 | author={Jiahui Zhang and Fangneng Zhan and Muyu Xu and Shijian Lu and Eric Xing}, 55 | year={2024}, 56 | eprint={2403.06908}, 57 | archivePrefix={arXiv}, 58 | primaryClass={cs.CV}, 59 | url={https://arxiv.org/abs/2403.06908}, 60 | shortname = {FreGS} 61 | } 62 | @misc{jung2024relaxing, 63 | title={Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting}, 64 | author={Jaewoo Jung and Jisang Han and Honggyu An and Jiwon Kang and Seonghoon Park and Seungryong Kim}, 65 | year={2024}, 66 | eprint={2403.09413}, 67 | archivePrefix={arXiv}, 68 | primaryClass={cs.CV}, 69 | url={https://arxiv.org/abs/2403.09413}, 70 | shortname = {RAIN-GS} 71 | } 72 | @misc{yang2024spectrally, 73 | title={Spectrally Pruned Gaussian Fields with Neural Compensation}, 74 | author={Runyi Yang and Zhenxin Zhu and Zhou Jiang and Baijun Ye and Xiaoxue Chen and Yifei Zhang and Yuantao Chen and Jian Zhao and Hao Zhao}, 75 | year={2024}, 76 | eprint={2405.00676}, 77 | archivePrefix={arXiv}, 78 | primaryClass={cs.CV}, 79 | url={https://arxiv.org/abs/2405.00676}, 80 | shortname = {SUNDAE} 81 | } 82 | @misc{li2024mvgsplatting, 83 | title={MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification}, 84 | author={Zhuoxiao Li and Shanliang Yao and Yijie Chu and Angel F. Garcia-Fernandez and Yong Yue and Eng Gee Lim and Xiaohui Zhu}, 85 | year={2024}, 86 | eprint={2407.11840}, 87 | archivePrefix={arXiv}, 88 | primaryClass={cs.CV}, 89 | url={https://arxiv.org/abs/2407.11840}, 90 | shortname = {MVG-Splatting} 91 | } 92 | @misc{liu2024atomgs, 93 | title={AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field}, 94 | author={Rong Liu and Rui Xu and Yue Hu and Meida Chen and Andrew Feng}, 95 | year={2024}, 96 | eprint={2405.12369}, 97 | archivePrefix={arXiv}, 98 | primaryClass={cs.CV}, 99 | url={https://rongliu-leo.github.io/AtomGS/}, 100 | shortname = {AtomGS} 101 | } 102 | @misc{zhang2024pixelgs, 103 | title={Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting}, 104 | author={Zheng Zhang and Wenbo Hu and Yixing Lao and Tong He and Hengshuang Zhao}, 105 | year={2024}, 106 | eprint={2403.15530}, 107 | archivePrefix={arXiv}, 108 | primaryClass={cs.CV}, 109 | url={https://arxiv.org/abs/2403.15530}, 110 | shortname = {Pixel-GS} 111 | } 112 | @misc{yan2024multiscale, 113 | title={Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering}, 114 | author={Zhiwen Yan and Weng Fei Low and Yu Chen and Gim Hee Lee}, 115 | year={2024}, 116 | eprint={2311.17089}, 117 | archivePrefix={arXiv}, 118 | primaryClass={cs.CV}, 119 | url={https://arxiv.org/abs/2311.17089}, 120 | shortname = {Multi-Scale GS} 121 | } 122 | @misc{feng2024flashgs, 123 | title={FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering}, 124 | author={Guofeng Feng and Siyan Chen and Rong Fu and Zimu Liao and Yi Wang and Tao Liu and Zhilin Pei and Hengjie Li and Xingcheng Zhang and Bo Dai}, 125 | year={2024}, 126 | eprint={2408.07967}, 127 | archivePrefix={arXiv}, 128 | primaryClass={cs.CV}, 129 | url={https://arxiv.org/abs/2408.07967}, 130 | shortname = {FlashGS} 131 | } 132 | @misc{seo2024flod, 133 | title={FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering}, 134 | author={Yunji Seo and Young Sun Choi and Hyun Seung Son and Youngjung Uh}, 135 | year={2024}, 136 | eprint={2408.12894}, 137 | archivePrefix={arXiv}, 138 | primaryClass={cs.CV}, 139 | url={https://arxiv.org/abs/2408.12894}, 140 | shortname = {FLoD} 141 | } 142 | @misc{ren2024octreegs, 143 | title={Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians}, 144 | author={Kerui Ren and Lihan Jiang and Tao Lu and Mulin Yu and Linning Xu and Zhangkai Ni and Bo Dai}, 145 | year={2024}, 146 | eprint={2403.17898}, 147 | archivePrefix={arXiv}, 148 | primaryClass={cs.CV}, 149 | url={https://arxiv.org/abs/2403.17898}, 150 | shortname = {Octree-GS} 151 | } 152 | @misc{zhang2024gaussianspa, 153 | title={GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting}, 154 | author={Yangming Zhang and Wenqi Jia and Wei Niu and Miao Yin}, 155 | year={2024}, 156 | eprint={2411.06019}, 157 | archivePrefix={arXiv}, 158 | primaryClass={cs.CV}, 159 | url={https://arxiv.org/abs/2411.06019}, 160 | shortname={GaussianSpa}, 161 | } 162 | -------------------------------------------------------------------------------- /plots/DeepBlending_compaction_LPIPS.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/plots/DeepBlending_compaction_LPIPS.pdf -------------------------------------------------------------------------------- /plots/DeepBlending_compaction_LPIPS_legend.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/w-m/3dgs-compression-survey/985610088c40a8c19fb3179141664239a7fa32ca/plots/DeepBlending_compaction_LPIPS_legend.pdf -------------------------------------------------------------------------------- /plots/DeepBlending_compaction_LPIPS_legend_h.pdf: 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video1ready = new Array(videoContainerList.length).fill(false); 4 | var video2ready = new Array(videoContainerList.length).fill(false); 5 | 6 | for (var i = 0; i < videoContainerList.length; i++) { 7 | var videoContainer = videoContainerList[i] 8 | var videoClipper = videoClipperList[i] 9 | 10 | videoContainer.addEventListener("mousemove", trackLocation(videoContainer, videoClipper)); 11 | videoContainer.addEventListener("touchstart", trackLocation(videoContainer, videoClipper)); 12 | videoContainer.addEventListener("touchmove", trackLocation(videoContainer, videoClipper)); 13 | 14 | var video1 = videoClipper.getElementsByTagName("video")[0] 15 | var video2 = videoContainer.getElementsByTagName("video")[0] 16 | 17 | // Check canplay event to know when both videos are ready to play and start them. 18 | 19 | video1.addEventListener('canplay', canplay1(video1, video2, i)); 20 | video2.addEventListener('canplay', canplay2(video1, video2, i)); 21 | // Stopped to load next frame, make the other video wait too. 22 | video1.addEventListener('waiting', () => { video2.pause(); }); 23 | video2.addEventListener('waiting', () => { video1.pause(); }); 24 | // Resumed from buffering, continue playing the other video too. 25 | video1.addEventListener('playing', () => { video2.play(); }); 26 | video2.addEventListener('playing', () => { video1.play(); }); 27 | } 28 | 29 | function tryStart(video1, video2, video1ready, video2ready) { 30 | if (video1ready && video2ready) { 31 | video1.play(); 32 | video2.play(); 33 | } 34 | } 35 | 36 | function canplay1(video1, video2, i) { 37 | return function () { 38 | video1ready[i] = true; 39 | tryStart(video1, video2, video1ready[i], video2ready[i]); 40 | } 41 | } 42 | 43 | function canplay2(video1, video2, i) { 44 | return function () { 45 | video2ready[i] = true; 46 | tryStart(video1, video2, video1ready[i], video2ready[i]); 47 | } 48 | } 49 | 50 | 51 | function trackLocation(videoContainer, videoClipper) { 52 | return function (e) { 53 | var clippedVideo = videoClipper.getElementsByTagName("video")[0] 54 | var rect = videoContainer.getBoundingClientRect() 55 | var position = ((e.pageX - rect.left) / videoContainer.offsetWidth) * 100 56 | 57 | if (position <= 100) { 58 | videoClipper.style.width = position + "%"; 59 | clippedVideo.style.width = ((100 / position) * 100) + "%"; 60 | clippedVideo.style.zIndex = 3; 61 | } 62 | } 63 | } -------------------------------------------------------------------------------- /project-page/static/js/index.js: -------------------------------------------------------------------------------- 1 | window.HELP_IMPROVE_VIDEOJS = false; 2 | 3 | 4 | $(document).ready(function() { 5 | // Check for click events on the navbar burger icon 6 | 7 | var options = { 8 | slidesToScroll: 1, 9 | slidesToShow: 1, 10 | loop: true, 11 | infinite: true, 12 | autoplay: true, 13 | autoplaySpeed: 5000, 14 | } 15 | 16 | // Initialize all div with carousel class 17 | var carousels = bulmaCarousel.attach('.carousel', options); 18 | 19 | bulmaSlider.attach(); 20 | 21 | }) 22 | -------------------------------------------------------------------------------- /project-page/static/js/vidsplit.js: -------------------------------------------------------------------------------- 1 | document.addEventListener('DOMContentLoaded', function () { 2 | const video = document.getElementsByClassName('sourceVideo')[0]; 3 | const canvas = document.getElementsByClassName('videoCanvas')[0]; 4 | const ctx = canvas.getContext('2d'); 5 | 6 | 7 | let splitX = canvas.width; // Initial split position 8 | 9 | video.addEventListener('loadedmetadata', () => { 10 | // Adjust canvas size based on the video frame, halving the height for display 11 | canvas.width = video.videoWidth / 2; 12 | canvas.height = video.videoHeight; 13 | }); 14 | 15 | video.play(); 16 | 17 | function draw() { 18 | if (video.paused || video.ended) return; 19 | ctx.clearRect(0, 0, canvas.width, canvas.height); 20 | 21 | // Draw the left part of the top video 22 | ctx.drawImage( 23 | video, 24 | 25 | 0, // sx 26 | 0, // sy 27 | splitX, // sWidth 28 | video.videoHeight, // sHeight 29 | 30 | 0, // dx 31 | 0, // dy 32 | splitX, //dWidth 33 | video.videoHeight // dHeight 34 | ); 35 | 36 | // Draw the right part of the bottom video 37 | ctx.drawImage( 38 | video, 39 | 40 | video.videoWidth / 2 + splitX, 41 | 0, 42 | video.videoWidth / 2 - splitX, 43 | video.videoHeight, 44 | 45 | splitX, 46 | 0, 47 | video.videoWidth / 2 - splitX, 48 | canvas.height 49 | ); 50 | 51 | 52 | // Draw a vertical line at the split 53 | ctx.beginPath(); // Begin a new path for the line 54 | ctx.moveTo(splitX, 0); // Move to the start point of the line at the top of the canvas 55 | ctx.lineTo(splitX, canvas.height); // Draw a line to the bottom of the canvas 56 | ctx.strokeStyle = 'white'; // Set the color of the line 57 | ctx.lineWidth = 1; // Set the line width 58 | ctx.stroke(); // Render the line 59 | 60 | requestAnimationFrame(draw); 61 | } 62 | 63 | video.onplay = () => { 64 | draw(); 65 | }; 66 | 67 | canvas.addEventListener('mousemove', function (e) { 68 | const rect = canvas.getBoundingClientRect(); 69 | splitX = e.clientX - rect.left; 70 | splitX = Math.max(0, Math.min(splitX, canvas.width)); 71 | draw(); 72 | }); 73 | }); 74 | -------------------------------------------------------------------------------- /project-page/static/js/vidsplit_multi.js: -------------------------------------------------------------------------------- 1 | let glob_splitX = [] 2 | 3 | document.addEventListener('DOMContentLoaded', function () { 4 | const videos = document.getElementsByClassName('sourceVideo'); 5 | const canvases = document.getElementsByClassName('videoCanvas'); 6 | const videoContainers = document.getElementsByClassName('video-container'); 7 | 8 | for (let i = 0; i < videos.length; i++) { 9 | let cur_video = videos[i]; 10 | let cur_canvas = canvases[i]; 11 | let cur_ctx = cur_canvas.getContext('2d'); 12 | let cur_video_container = videoContainers[i]; 13 | glob_splitX.push(50); 14 | 15 | cur_video.addEventListener('loadedmetadata', load_event(cur_canvas, cur_video_container)); 16 | cur_video.play(); 17 | cur_video.addEventListener('playing', play_event(cur_video, cur_canvas, cur_ctx, i, cur_video_container)); 18 | cur_canvas.addEventListener('mousemove', move_event(cur_canvas, i, cur_video_container)) 19 | } 20 | }); 21 | 22 | function load_event(canvas, video_container){ 23 | return () => { 24 | console.log(video_container.offsetWidth) 25 | canvas.width = video_container.offsetWidth; 26 | canvas.height = video_container.offsetHeight; 27 | } 28 | } 29 | 30 | function play_event(video, canvas, ctx, index, video_container) { 31 | return () => { 32 | setInterval(() => { 33 | draw(video, canvas, ctx, glob_splitX[index], video_container); 34 | }, 5); 35 | } 36 | } 37 | 38 | function move_event(canvas, index){ 39 | return (e) => { 40 | const rect = canvas.getBoundingClientRect(); 41 | glob_splitX[index] = e.clientX - rect.left; 42 | glob_splitX[index] = Math.max(0, Math.min(glob_splitX[index], canvas.width)); 43 | } 44 | } 45 | 46 | function draw(video, canvas, ctx, splitX, video_container) { 47 | if (video.paused || video.ended) return; 48 | if (ctx === undefined) return; 49 | // console.log(index, glob_splitX[index]) 50 | 51 | ctx.clearRect(0, 0, canvas.width, canvas.height); 52 | splitX_imgSpace = splitX * (0.5 * video.videoWidth) / video_container.offsetWidth 53 | ctx.drawImage( 54 | video, 55 | 0, // sx 56 | 0, // sy 57 | splitX_imgSpace, // sWidth 58 | video.videoHeight, // sHeight 59 | 0, // dx 60 | 0, // dy 61 | splitX, //dWidth 62 | canvas.height // dHeight 63 | ); 64 | 65 | ctx.drawImage( 66 | video, 67 | 68 | video.videoWidth / 2 + splitX_imgSpace, 69 | 0, 70 | video.videoWidth / 2 - splitX_imgSpace, 71 | video.videoHeight, 72 | 73 | splitX, 74 | 0, 75 | canvas.width - splitX, 76 | canvas.height 77 | ); 78 | 79 | // Draw a vertical line at the split 80 | ctx.beginPath(); 81 | ctx.moveTo(splitX, 0); 82 | ctx.lineTo(splitX, canvas.height); 83 | ctx.strokeStyle = 'white'; 84 | ctx.lineWidth = 2; 85 | ctx.stroke(); 86 | requestAnimationFrame(draw); 87 | } 88 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | beautifulsoup4 2 | bibtexparser 3 | Jinja2 4 | numpy 5 | pandas 6 | PyYAML 7 | requests 8 | TexSoup 9 | Pillow -------------------------------------------------------------------------------- /results/DeepBlending.csv: -------------------------------------------------------------------------------- 1 | Method,Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians,Data Source,Comment 2 | chen2024hac,-lowrate,29.98,0.902,0.269,4561148,568841.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 3 | chen2024hac,-highrate,30.34,0.906,0.258,6660817,624482.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 4 | chen2025fcgs,-lowrate,29.27,0.893,0.257,30107239,2807177.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 5 | chen2025fcgs,-highrate,29.58,0.899,0.248,54475097,2807177.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 6 | chen2025hac-plus,-lowrate,30.16,0.907,0.266,3051199,328522.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 7 | chen2025hac-plus,-highrate,30.34,0.911,0.254,5539942,642515.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 8 | cheng2024gaussianpro,,29.84,0.912,0.225,607571149,2449878.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 9 | cheng2024gaussianpro,Baseline,29.79,0.913,0.222,640517407,2582735.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 10 | fan2024lightgaussian,,,,,,,, 11 | fang2024minisplatting,Baseline,29.98,0.908,0.253,,350000.0,https://arxiv.org/pdf/2403.14166, 12 | fang2024minisplatting,-D,29.88,0.906,0.211,,4630000.0,https://arxiv.org/pdf/2403.14166, 13 | girish2024eagles,-Small,29.92,0.90,0.25,33000000,1190000.0,https://arxiv.org/pdf/2312.04564, 14 | girish2024eagles,Baseline,29.86,0.91,0.25,52000000,,https://arxiv.org/pdf/2312.04564, 15 | kerbl3Dgaussians,Baseline,29.41,0.903,0.243,676000000,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_kerbl20233dgs, 16 | kim2024CVPR,Baseline,29.71,0.902,0.255,72000000,644000.0,https://openaccess.thecvf.com/content/CVPR2024W/3DMV/papers/Kim_Color-cued_Efficient_Densification_Method_for_3D_Gaussian_Splatting_CVPRW_2024_paper.pdf, 17 | lee2024compact,+PP,29.73,0.900,0.258,23800000,,https://arxiv.org/src/2311.13681, 18 | lee2024compact,Baseline,29.79,0.901,0.258,43200000,1058679.0,https://arxiv.org/src/2311.13681, 19 | lee2025compression3dgaussiansplatting,Baseline,29.81,0.906,0.251,9045757,2468145.0,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_lee2025compression3dgaussiansplatting, 20 | liu2024compgs,,29.30,0.895,0.293,6320023,298689.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 21 | liu2024compgs,,29.40,0.897,0.289,7152496,266042.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 22 | liu2024compgs,Baseline,29.69,0.901,0.279,9191336,229486.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 23 | liu2024hemgs,-lowrate,30.24,0.908,0.266,2993003,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 24 | liu2024hemgs,-highrate,30.37,0.911,0.253,6696783,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 25 | lu2024scaffold,Baseline,30.21,0.906,0.254,66000000,,https://arxiv.org/pdf/2312.00109, 26 | morgenstern2024compact, w/o SH,29.12,0.892,0.270,5712320,800173.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 27 | morgenstern2024compact,Baseline,29.26,0.894,0.268,17736797,890433.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 28 | navaneet2023compact3d, 16K,29.90,0.906,0.252,12000000,,https://arxiv.org/src/2311.18159, 29 | navaneet2023compact3d, 32K,29.90,0.907,0.251,13000000,554000.0,https://arxiv.org/src/2311.18159, 30 | niedermayr2024compressed,Baseline,29.381,0.898,0.253,25299000,,https://arxiv.org/pdf/2401.02436, 31 | papantonakis2024reducing,Baseline,29.63,0.902,0.249,18000000,1010000.0,https://repo-sam.inria.fr/fungraph/reduced_3dgs/reduced_3DGS_i3d.pdf, 32 | pateux2025bogauss,,30.25,0.897,0.294,,84000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 33 | pateux2025bogauss,,30.63,0.904,0.276,,140000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 34 | pateux2025bogauss, Tiny,30.96,0.908,0.259,,280000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 35 | pateux2025bogauss, Light,31.10,0.912,0.247,,560000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 36 | pateux2025bogauss,,31.24,0.914,0.237,,1120000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 37 | pateux2025bogauss,,31.25,0.915,0.232,,1680000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 38 | pateux2025bogauss,,31.22,0.915,0.230,,2240000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 39 | pateux2025bogauss,,31.25,0.915,0.229,,2800000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 40 | ren2024octreegs,Baseline,30.49,0.912,0.241,,112000.0,https://arxiv.org/pdf/2403.17898, 41 | taming20243dgs,Baseline,27.79,0.822,0.263,,270000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 42 | taming20243dgs, (Big),30.14,0.907,0.235,,2810000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 43 | wang2024contextgs,_lowrate,30.09,0.907,0.265,3654759,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 44 | wang2024contextgs,_highrate,30.41,0.909,0.259,6864817,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 45 | wang2024end,,29.35,0.895,0.277,4135678,318768.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 46 | wang2024end,,29.49,0.899,0.265,6706772,528719.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 47 | wang2024end,,29.59,0.902,0.257,10959800,887813.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 48 | wang2024end,Baseline,29.63,0.902,0.252,17998224,1474690.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 49 | xie2024mesongs, c3,29.48,0.903,0.252,29011600,2022966.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 50 | xie2024mesongs, c1,29.50,0.903,0.251,31071609,2166331.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 51 | zhang2024gaussianspa,Baseline,30.13,0.913,0.236,130933503,527952.0,https://raw.githubusercontent.com/noodle-lab/GaussianSpa/main/results, 52 | -------------------------------------------------------------------------------- /results/MipNeRF360.csv: -------------------------------------------------------------------------------- 1 | Method,Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians,Data Source,Comment 2 | chen2024hac,-lowrate,27.53,0.807,0.238,16005406,2166474.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 3 | chen2024hac,-highrate,27.77,0.811,0.230,22935654,2264748.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 4 | chen2025fcgs,-lowrate,27.05,0.798,0.237,36321508,3133558.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 5 | chen2025fcgs,-highrate,27.39,0.806,0.226,67157681,3133558.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 6 | chen2025hac-plus,-lowrate,27.60,0.803,0.253,8742363,681811.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 7 | chen2025hac-plus,-highrate,27.82,0.811,0.231,19379619,1852696.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 8 | cheng2024gaussianpro,,27.46,0.813,0.217,750575361,3026509.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 9 | cheng2024gaussianpro,Baseline,27.43,0.813,0.219,844072223,3403512.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 10 | fan2024lightgaussian,Baseline,27.28,0.805,0.243,42000000,,https://arxiv.org/src/2311.17245, 11 | fang2024minisplatting,Baseline,27.34,0.822,0.217,,490000.0,https://arxiv.org/pdf/2403.14166, 12 | fang2024minisplatting,-D,27.51,0.831,0.176,,4690000.0,https://arxiv.org/pdf/2403.14166, 13 | girish2024eagles,-Small,26.94,0.80,0.25,47000000,1330000.0,https://arxiv.org/pdf/2312.04564, 14 | girish2024eagles,Baseline,27.23,0.81,0.24,54000000,,https://arxiv.org/pdf/2312.04564, 15 | hu2024gsplat,,26.64,0.788,0.270,6916294,360000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 16 | hu2024gsplat,,26.88,0.796,0.256,8796870,490000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 17 | hu2024gsplat,-1.00M,27.29,0.811,0.229,16038022,1000000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 18 | hu2024gsplat,,27.70,0.825,0.197,57812682,4000000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 19 | kerbl3Dgaussians,Baseline,27.21,0.815,0.214,734000000,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_kerbl20233dgs, 20 | kim2024CVPR,Baseline,27.07,0.797,0.249,73000000,646000.0,https://openaccess.thecvf.com/content/CVPR2024W/3DMV/papers/Kim_Color-cued_Efficient_Densification_Method_for_3D_Gaussian_Splatting_CVPRW_2024_paper.pdf, 21 | lee2024compact,+PP,27.03,0.797,0.247,29100000,,https://arxiv.org/src/2311.13681, 22 | lee2024compact,Baseline,27.08,0.798,0.247,48800000,1388162.0,https://arxiv.org/src/2311.13681, 23 | lee2025compression3dgaussiansplatting,Baseline,27.30,0.810,0.236,10260579,2591839.0,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_lee2025compression3dgaussiansplatting, 24 | liu2024atomgs,Baseline,27.38,0.816,0.211,778950993,3140929.0,https://arxiv.org/pdf/2405.12369,(#Gaussians and Size in Email to Yi-Hsin + MB are actually MiB as they then result to the 248bytes/G)) 25 | liu2024compgs,,26.37,0.778,0.276,9257029,457368.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 26 | liu2024compgs,,26.78,0.791,0.259,11551380,475965.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 27 | liu2024compgs,Baseline,27.26,0.803,0.239,17301474,493205.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 28 | liu2024hemgs,-lowrate,27.75,0.806,0.248,12538557,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 29 | liu2024hemgs,-highrate,27.93,0.813,0.230,21000111,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 30 | lu2024scaffold,Baseline,27.50,0.806,0.252,156000000,,https://arxiv.org/pdf/2403.14530,Results taken from HAC paper as they only evaluated on 7 scenes 31 | morgenstern2024compact, w/o SH,26.56,0.791,0.241,16684812,2149729.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 32 | morgenstern2024compact,Baseline,27.08,0.799,0.230,40284501,2176333.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 33 | navaneet2023compact3d, 16K,27.03,0.804,0.243,18000000,,https://arxiv.org/src/2311.18159, 34 | navaneet2023compact3d, 32K,27.12,0.806,0.240,19000000,845000.0,https://arxiv.org/src/2311.18159, 35 | niedermayr2024compressed,Baseline,26.981,0.801,0.238,28803000,,https://arxiv.org/pdf/2401.02436, 36 | papantonakis2024reducing,Baseline,27.10,0.809,0.226,29000000,1460000.0,https://repo-sam.inria.fr/fungraph/reduced_3dgs/reduced_3DGS_i3d.pdf, 37 | pateux2025bogauss,,26.83,0.766,0.305,,96200.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 38 | pateux2025bogauss,,27.10,0.783,0.282,,160333.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 39 | pateux2025bogauss, Tiny,27.54,0.804,0.251,,320667.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 40 | pateux2025bogauss, Light,28.26,0.824,0.216,,641333.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 41 | pateux2025bogauss,,28.59,0.836,0.191,,1282667.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 42 | pateux2025bogauss,,28.73,0.840,0.181,,1924000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 43 | pateux2025bogauss,,28.78,0.843,0.175,,2565333.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 44 | pateux2025bogauss,,28.82,0.844,0.171,,3206667.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 45 | ren2024octreegs,Baseline,28.05,0.819,0.217,,657000.0,https://arxiv.org/pdf/2403.17898, 46 | taming20243dgs,Baseline,27.29,0.799,0.253,,630000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 47 | taming20243dgs, (Big),27.79,0.822,0.205,,3310000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 48 | wang2024contextgs,_lowrate,27.62,0.808,0.237,13297458,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 49 | wang2024contextgs,_highrate,27.75,0.811,0.231,19308117,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 50 | wang2024end,,26.03,0.764,0.299,6162424,479851.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 51 | wang2024end,,26.50,0.784,0.268,9761150,776862.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 52 | wang2024end,,26.87,0.796,0.248,15351860,1237152.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 53 | wang2024end,Baseline,27.05,0.802,0.239,23460290,1859329.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 54 | xie2024mesongs, c3,26.99,0.797,0.246,25929623,1870378.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 55 | xie2024mesongs, c1,26.99,0.796,0.247,28484305,2082024.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 56 | zhang2024gaussianspa,Baseline,27.83,0.827,0.214,574210407,558873.0,https://raw.githubusercontent.com/noodle-lab/GaussianSpa/main/results, 57 | -------------------------------------------------------------------------------- /results/SyntheticNeRF.csv: -------------------------------------------------------------------------------- 1 | Method,Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians,Data Source,Comment 2 | chen2024hac,-lowrate,33.24,0.967,0.037,1236009,99924,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 3 | chen2024hac,-highrate,33.71,0.968,0.034,1950692,143127,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 4 | chen2025hac-plus,-lowrate,32.77,0.965,0.041,793523,51208, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 5 | chen2025hac-plus,-highrate,33.76,0.969,0.033,1934334,205794, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 6 | fan2024lightgaussian,Baseline,32.725,0.965,0.037,7838000,,https://arxiv.org/pdf/2311.17245,"Taken from Table 6, avg of per-scene results" 7 | kerbl3Dgaussians,Baseline,33.31,NaN,NaN,,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_kerbl20233dgs, 8 | lee2024compact,+PP,32.88,0.968,0.034,2799698,,https://arxiv.org/pdf/2403.14530, 9 | lee2024compact,Baseline,33.33,0.968,0.034,5809111,,https://arxiv.org/pdf/2403.14530, 10 | liu2024hemgs,-lowrate,33.58,0.967,0.037,1316251,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 11 | liu2024hemgs,-highrate,33.71,0.968,0.035,1644115,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 12 | morgenstern2024compact, w/o SH,31.37,0.959,0.043,1978015,175871,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 13 | morgenstern2024compact,Baseline,33.23,0.966,0.034,4132631,157322,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 14 | niedermayr2024compressed,Baseline,32.936,0.967,0.033,3686000,,https://arxiv.org/pdf/2401.02436, 15 | sun2024f3dgs,-CP-16,32.42,0.964,0.040,6354370,,https://arxiv.org/pdf/2405.17083,[N/T][N/P] PSNR 32.42 in Table 1 and 32.48 in Table 9 16 | wang2024end,,32.01,0.961,0.043,1016378,44811,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 17 | wang2024end,,32.56,0.964,0.038,1331577,64772,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 18 | wang2024end,,32.97,0.967,0.035,1838843,99988,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 19 | wang2024end,Baseline,33.12,0.967,0.035,2314380,132811,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 20 | xie2024mesongs, c3,32.96,0.968,0.033,3498725,207100,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 21 | xie2024mesongs, c1,32.94,0.968,0.033,3873306,235531,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 22 | -------------------------------------------------------------------------------- /results/TanksAndTemples.csv: -------------------------------------------------------------------------------- 1 | Method,Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians,Data Source,Comment 2 | chen2024hac,-lowrate,24.04,0.846,0.187,8493728,848774.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 3 | chen2024hac,-highrate,24.40,0.853,0.177,11789455,1025887.0,https://raw.githubusercontent.com/YihangChen-ee/HAC/main/results, 4 | chen2025fcgs,-lowrate,23.48,0.833,0.193,18761122,1826652.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 5 | chen2025fcgs,-highrate,23.62,0.839,0.184,33576977,1826652.0,https://raw.githubusercontent.com/YihangChen-ee/FCGS/main/results, 6 | chen2025hac-plus,-lowrate,24.22,0.849,0.190,5426905,511289.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 7 | chen2025hac-plus,-highrate,24.33,0.853,0.181,7260969,716161.0, https://raw.githubusercontent.com/YihangChen-ee/HAC-plus/main/results, 8 | cheng2024gaussianpro,,23.71,0.857,0.186,349679125,1410003.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 9 | cheng2024gaussianpro,Baseline,24.09,0.862,0.185,357428101,1441225.0,https://raw.githubusercontent.com/kcheng1021/GaussianPro/version1.0/results, 10 | fan2024lightgaussian,Baseline,23.11,0.817,0.231,22000000,,https://arxiv.org/src/2311.17245, 11 | fang2024minisplatting,Baseline,23.18,0.835,0.202,,200000.0,https://arxiv.org/pdf/2403.14166, 12 | fang2024minisplatting,-D,23.23,0.853,0.140,,4280000.0,https://arxiv.org/pdf/2403.14166, 13 | girish2024eagles,-Small,23.1,0.82,0.22,19000000,650000.0,https://arxiv.org/pdf/2312.04564, 14 | girish2024eagles,Baseline,23.37,0.84,0.20,29000000,,https://arxiv.org/pdf/2312.04564, 15 | hu2024gsplat,,23.54,0.838,0.200,6875669,360000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 16 | hu2024gsplat,,23.62,0.845,0.188,8728572,490000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 17 | hu2024gsplat,-1.00M,24.03,0.857,0.163,16100628,1000000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 18 | hu2024gsplat,,24.47,0.872,0.132,58239022,4000000.0,https://raw.githubusercontent.com/nerfstudio-project/gsplat/main/examples/benchmarks/compression/results, 19 | kerbl3Dgaussians,Baseline,23.14,0.841,0.183,411000000,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_kerbl20233dgs, 20 | kim2024CVPR,Baseline,23.18,0.830,0.198,42000000,370000.0,https://openaccess.thecvf.com/content/CVPR2024W/3DMV/papers/Kim_Color-cued_Efficient_Densification_Method_for_3D_Gaussian_Splatting_CVPRW_2024_paper.pdf, 21 | lee2024compact,+PP,23.32,0.831,0.202,20900000,,https://arxiv.org/src/2311.13681, 22 | lee2024compact,Baseline,23.32,0.831,0.201,39400000,836296.0,https://arxiv.org/src/2311.13681, 23 | lee2025compression3dgaussiansplatting,Baseline,23.63,0.841,0.192,7831133,1543041.0,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_lee2025compression3dgaussiansplatting, 24 | liu2024atomgs,Baseline,23.70,0.849,0.166,367206072,1480685.0,https://arxiv.org/pdf/2405.12369,(#Gaussians and Size in Email to Yi-Hsin + MB are actually MiB as they then result to the 248bytes/G) 25 | liu2024compgs,,23.11,0.815,0.236,6177041,233056.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 26 | liu2024compgs,,23.39,0.828,0.219,7624788,244759.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 27 | liu2024compgs,Baseline,23.70,0.837,0.208,10070163,235240.0,https://raw.githubusercontent.com/LiuXiangrui/CompGS/main/results, 28 | liu2024hemgs,-lowrate,24.42,0.848,0.192,6034293,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 29 | liu2024hemgs,-highrate,24.58,0.856,0.176,10136479,,https://raw.githubusercontent.com/w-m/3dgs-compression-survey/main/sources/results_liu2024hemgs, 30 | lu2024scaffold,Baseline,23.96,0.853,0.177,87000000,,https://arxiv.org/pdf/2312.00109, 31 | morgenstern2024compact, w/o SH,23.15,0.828,0.198,9293275,1207490.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 32 | morgenstern2024compact,Baseline,23.56,0.837,0.186,22778535,1242209.0,https://raw.githubusercontent.com/fraunhoferhhi/Self-Organizing-Gaussians/main/results, 33 | navaneet2023compact3d, 16K,23.39,0.836,0.200,12000000,,https://arxiv.org/src/2311.18159, 34 | navaneet2023compact3d, 32K,23.44,0.838,0.198,13000000,520000.0,https://arxiv.org/src/2311.18159, 35 | niedermayr2024compressed,Baseline,23.324,0.832,0.194,17282000,,https://arxiv.org/pdf/2401.02436, 36 | papantonakis2024reducing,Baseline,23.57,0.840,0.188,14000000,680000.0,https://repo-sam.inria.fr/fungraph/reduced_3dgs/reduced_3DGS_i3d.pdf, 37 | pateux2025bogauss,,24.31,0.823,0.267,,55050.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 38 | pateux2025bogauss,,24.59,0.829,0.259,,91750.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 39 | pateux2025bogauss, Tiny,24.85,0.837,0.244,,183500.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 40 | pateux2025bogauss, Light,25.50,0.853,0.215,,367000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 41 | pateux2025bogauss,,25.79,0.862,0.193,,734000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 42 | pateux2025bogauss,,25.90,0.867,0.182,,1101000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 43 | pateux2025bogauss,,25.97,0.870,0.175,,1468000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 44 | pateux2025bogauss,,25.99,0.872,0.170,,1835000.0,https://raw.githubusercontent.com/spateux/3dgs-compression-survey/pateux2025bogauss/sources/results_pateux2025bogauss, 45 | ren2024octreegs,Baseline,24.68,0.866,0.153,,443000.0,https://arxiv.org/pdf/2403.17898, 46 | sun2024f3dgs,-CP-16,30.29,0.957,0.061,11471421,,https://arxiv.org/pdf/2405.17083,[N/T][N/P] 47 | taming20243dgs,Baseline,23.89,0.835,0.207,,290000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 48 | taming20243dgs, (Big),24.04,0.851,0.170,,1840000.0,https://humansensinglab.github.io/taming-3dgs/docs/paper.pdf, 49 | wang2024contextgs,_lowrate,24.12,0.849,0.186,9902175,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 50 | wang2024contextgs,_highrate,24.29,0.855,0.176,12377181,,https://raw.githubusercontent.com/wyf0912/ContextGS/main/results, 51 | wang2024end,,22.98,0.812,0.234,3737081,263067.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 52 | wang2024end,,23.14,0.823,0.215,5492385,395042.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 53 | wang2024end,,23.28,0.831,0.202,8017022,597209.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 54 | wang2024end,Baseline,23.34,0.835,0.195,12022711,907503.0,https://raw.githubusercontent.com/USTC-IMCL/RDO-Gaussian/main/results, 55 | xie2024mesongs, c3,23.29,0.835,0.197,17357031,1162500.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 56 | xie2024mesongs, c1,23.31,0.835,0.196,18465486,1239197.0,https://raw.githubusercontent.com/ShuzhaoXie/MesonGS/main/results, 57 | zhang2024gaussianspa,Baseline,24.00,0.850,0.172,429930939,322296.0,https://raw.githubusercontent.com/noodle-lab/GaussianSpa/main/results, 58 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/DeepBlending/drjohnson.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,28.766,0.899,0.244,676000000, 2975634.50 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/DeepBlending/playroom.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,30.044,0.906,0.241,676000000, 2975634.50 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/bicycle.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,25.246,0.771,0.205,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/bonsai.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K, 31.980,0.938,0.205,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/counter.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,28.700,0.905,0.204,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/flowers.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,21.520,0.605,0.336,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/garden.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,27.410,0.868, 0.103,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/kitchen.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K, 30.317, 0.922,0.129,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/room.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,30.632,0.914,0.220,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/stump.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,26.550,0.775,0.210,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/MipNeRF360/treehill.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,22.490,0.638, 0.317,734000000, 3362470.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/chair.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,35.83,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/drums.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,26.15,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/ficus.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,34.87,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/hotdog.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,37.72,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/lego.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,35.78,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/materials.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,30.00,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/mic.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,35.36,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/SyntheticNeRF/ship.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,30.80,,,, # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/TanksAndTemples/train.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K, 21.097,0.802,0.218,411000000, 1783867.00 # Size and num Gaussians are avarages over dataset -------------------------------------------------------------------------------- /sources/results_kerbl20233dgs/TanksAndTemples/truck.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | 3DGS-30K,25.187,0.879,0.148,411000000, 1783867.00 # Size and num Gaussians are avarages over dataset 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/DeepBlending/drjohnson.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,29.219094,0.903902,0.250259,9848204,2847825 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/DeepBlending/playroom.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,30.410577,0.908728,0.252095,8243309,2088465 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/bicycle.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,25.190952,0.745905,0.264791,10292285,4783688 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/bonsai.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,31.711295,0.934491,0.201964,7145961,1098312 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/counter.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,28.471095,0.897955,0.207895,6753886,1089419 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/flowers.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,21.369522,0.593332,0.379554,10763881,2714616 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/garden.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,27.60945,0.853095,0.133224,15770587,4378120 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/kitchen.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,30.727954,0.915788,0.137177,8096579,1473572 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/room.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,30.987772,0.921672,0.216778,8376863,1132558 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/stump.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,26.587895,0.780965,0.235917,14706383,3804978 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/MipNeRF360/treehill.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,23.06678,0.647905,0.345786,10438783,2851291 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/TanksAndTemples/train.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,22.07609,0.800967,0.225975,6516061,875698 3 | -------------------------------------------------------------------------------- /sources/results_lee2025compression3dgaussiansplatting/TanksAndTemples/truck.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | CodecGS,25.178092,0.881772,0.157752,9146204,2210383 3 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/DeepBlending/drjohnson.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,29.93667603,0.909033,0.248555124,8108428.4928, 3 | HEMGS-lowrate,29.89328003,0.906858087,0.260687381,3600390.5536, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/DeepBlending/playroom.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,30.81164169,0.912434757,0.257877886,5285137.6128, 3 | HEMGS-lowrate,30.59244156,0.908614576,0.270519078,2385615.2576, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/bicycle.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,25.28336334,0.755925536,0.24879463,36673316.4544, 3 | HEMGS-lowrate,25.25536537,0.751764834,0.263061643,23199009.9968, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/bonsai.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,33.33193588,0.948720574,0.179509044,11727483.6992, 3 | HEMGS-lowrate,32.53875732,0.942095757,0.191136345,6904558.3872, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/counter.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,29.78606796,0.915775299,0.187746152,10091495.424, 3 | HEMGS-lowrate,29.56256676,0.907561958,0.203057766,6083628.2368, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/flowers.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,21.11848831,0.566370308,0.382725537,26982691.6352, 3 | HEMGS-lowrate,21.38053131,0.57601285,0.385612458,15784109.6704, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/garden.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,27.34413147,0.84871608,0.142259002,31191570.8416, 3 | HEMGS-lowrate,27.32735634,0.835676491,0.17310448,19087648.3584, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/kitchen.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,32.00621033,0.93118906,0.121568434,11775193.9072, 3 | HEMGS-lowrate,31.50585175,0.923004806,0.135863855,7020006.6048, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/room.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,32.50338745,0.928303719,0.197183773,7447196.4672, 3 | HEMGS-lowrate,32.06850433,0.918864608,0.219952509,3852468.224, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/stump.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,26.76065254,0.77333653,0.254433572,23079262.6176, 3 | HEMGS-lowrate,26.83481026,0.768339813,0.275923461,13377522.8928, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/MipNeRF360/treehill.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,23.26482582,0.64636445,0.35234502,30032789.504, 3 | HEMGS-lowrate,23.25191689,0.634871244,0.385386825,17538062.7456, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/chair.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,35.22154617,0.985343635,0.013840275,1603901.8496, 3 | HEMGS-lowrate,34.71944046,0.982963741,0.017206563,1115265.4336, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/drums.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,26.4240799,0.950210929,0.043419953,2283169.3824, 3 | HEMGS-lowrate,26.33442879,0.949800253,0.044704694,1683488.768, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/ficus.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,35.6777153,0.98665309,0.01272146,1318584.32, 3 | HEMGS-lowrate,35.7024498,0.986510634,0.013206508,1032008.4992, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/hotdog.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,37.60601044,0.98237288,0.026946751,902089.9328, 3 | HEMGS-lowrate,37.21546936,0.98093915,0.030149316,680630.6816, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/lego.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,35.30571747,0.97948575,0.021338431,1417884.4672, 3 | HEMGS-lowrate,35.74531555,0.981151521,0.018607821,1865940.992, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/materials.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,30.81142807,0.962890148,0.037565067,1924556.3904, 3 | HEMGS-lowrate,30.76364326,0.962741375,0.038765419,1467272.3968, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/mic.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,36.99309158,0.992209196,0.007590758,917399.1424, 3 | HEMGS-lowrate,36.57004166,0.991459966,0.008721889,677694.6688, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/SyntheticNeRF/ship.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,31.64896774,0.905021369,0.117756337,2785332.4288, 3 | HEMGS-lowrate,31.54930687,0.902453005,0.12339329,2007708.4672, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/TanksAndTemples/train.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,22.9168396,0.826961339,0.205098957,8819048.448, 3 | HEMGS-lowrate,22.75288391,0.818762779,0.219016984,5633893.9904, 4 | -------------------------------------------------------------------------------- /sources/results_liu2024hemgs/TanksAndTemples/truck.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | HEMGS-highrate,26.23481369,0.885105014,0.145949334,11453910.2208, 3 | HEMGS-lowrate,26.082407,0.877734244,0.164029494,6434691.4816, 4 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/DeepBlending/drjohnson.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,29.185324969697,0.890542515151515,0.299548787878788,,98100 3 | ,29.6216798484848,0.898039818181818,0.278361696969697,,163500 4 | Tiny,29.9790327575757,0.903869727272727,0.260049242424242,,327000 5 | Light,30.1297724242424,0.907775636363636,0.246265393939394,,654000 6 | ,30.3205277878788,0.911075606060606,0.234425151515152,,1308000 7 | ,30.3462668787879,0.912259181818182,0.229051060606061,,1962000 8 | ,30.311858030303,0.912962090909091,0.225919272727273,,2616000 9 | ,30.3423764848485,0.913106,0.223744939393939,,3270000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/DeepBlending/playroom.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,31.3180646206897,0.904380068965517,0.287683655172414,,69900 3 | ,31.6391423103448,0.909024344827586,0.272940586206896,,116500 4 | Tiny,31.9424063103448,0.91296775862069,0.258431068965517,,233000 5 | Light,32.0777541034483,0.915604586206896,0.247230448275862,,466000 6 | ,32.1496220689655,0.916638137931034,0.238808172413793,,932000 7 | ,32.1545812758621,0.917259413793103,0.235171,,1398000 8 | ,32.1197462758621,0.91717575862069,0.234489724137931,,1864000 9 | ,32.1491294482759,0.917242068965517,0.235020896551724,,2330000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/bicycle.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,25.00050476,0.67534516,0.35011872,,179700 3 | ,25.44134532,0.70699172,0.31635612,,299500 4 | Tiny,25.95221248,0.74462316,0.27010412,,599000 5 | Light,26.3850462,0.77586608,0.22687036,,1198000 6 | ,26.65171476,0.79683388,0.19291656,,2396000 7 | ,26.75285152,0.80556148,0.17797516,,3594000 8 | ,26.80051392,0.8098786,0.16972044,,4792000 9 | ,26.82066088,0.8122738,0.16444392,,5990000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/bonsai.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,31.1594358378378,0.930692108108108,0.216909891891892,,35700 3 | ,31.1845052432432,0.930802810810811,0.216411891891892,,59500 4 | Tiny,31.155160027027,0.930743432432432,0.216257945945946,,119000 5 | Light,32.507106027027,0.943179243243243,0.189346540540541,,238000 6 | ,33.2778414054054,0.950375540540541,0.169217864864865,,476000 7 | ,33.5658554054054,0.953105351351351,0.161337540540541,,714000 8 | ,33.6998635135135,0.954430945945946,0.156681324324324,,952000 9 | ,33.7456658648649,0.95484427027027,0.153586324324324,,1190000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/counter.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,28.0646987,0.8891016,0.230204866666667,,35700 3 | ,28.0762853333333,0.8891057,0.230284,,59500 4 | Tiny,28.226233,0.8917171,0.2269105,,119000 5 | Light,29.1198420666667,0.9078016,0.1947149,,238000 6 | ,29.5520278666667,0.916138533333333,0.1756772,,476000 7 | ,29.6839344333333,0.919308766666667,0.168220766666667,,714000 8 | ,29.7774227666667,0.921257933333333,0.163915466666667,,952000 9 | ,29.8335592,0.922328866666667,0.161011833333333,,1190000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/flowers.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,20.8563144545455,0.515509181818182,0.460802681818182,,108600 3 | ,21.3041179090909,0.552853181818182,0.418962545454545,,181000 4 | Tiny,21.8578832727273,0.5952605,0.367701318181818,,362000 5 | Light,22.2466940909091,0.626042,0.323231727272727,,724000 6 | ,22.4383104545455,0.645763727272727,0.290066,,1448000 7 | ,22.4820761818182,0.653804863636364,0.275699863636364,,2172000 8 | ,22.5201467272727,0.659499409090909,0.265962590909091,,2896000 9 | ,22.5236482272727,0.662191909090909,0.258729545454545,,3620000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/garden.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,25.87904775,0.759352333333334,0.293635333333333,,152100 3 | ,26.645874875,0.800645291666667,0.235112625,,253500 4 | Tiny,27.4296307916667,0.840060166666667,0.169213125,,507000 5 | Light,28.0142725416667,0.864485166666667,0.124728458333333,,1014000 6 | ,28.318546,0.878244416666667,0.100558208333333,,2028000 7 | ,28.47973925,0.883363708333333,0.092513125,,3042000 8 | ,28.5510868333333,0.8855355,0.0889417916666667,,4056000 9 | ,28.549813125,0.886756458333333,0.0869799583333333,,5070000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/kitchen.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,29.9961946857143,0.908731228571429,0.161719114285714,,48300 3 | ,29.9741231714286,0.908505914285714,0.162415171428571,,80500 4 | Tiny,29.9772883142857,0.908784571428571,0.162249857142857,,161000 5 | Light,31.9061827714286,0.925899514285714,0.1294296,,322000 6 | ,32.4269979428571,0.933203371428571,0.114689428571429,,644000 7 | ,32.6815038571429,0.936054942857143,0.109013742857143,,966000 8 | ,32.7262962571429,0.937479914285714,0.106317571428571,,1288000 9 | ,32.8554876285714,0.938468342857143,0.1045836,,1610000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/room.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,30.8172811025641,0.903561256410256,0.249050666666667,,46500 3 | ,30.8407406923077,0.903361051282051,0.249339487179487,,77500 4 | Tiny,32.0922501025641,0.917463,0.219814051282051,,155000 5 | Light,32.5675466666667,0.924587307692308,0.200447820512821,,310000 6 | ,32.8403205128205,0.930893153846154,0.183846230769231,,620000 7 | ,33.1537252564103,0.934256897435897,0.17523782051282,,930000 8 | ,33.1076922564103,0.935338153846154,0.171110564102564,,1240000 9 | ,33.2642814358974,0.937063461538461,0.16771141025641,,1550000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/stump.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,26.1519426875,0.713044625,0.3303685625,,146100 3 | ,26.668156875,0.7439690625,0.2899321875,,243500 4 | Tiny,27.236246375,0.7759608125,0.24147175,,487000 5 | Light,27.528464,0.7947088125,0.2078458125,,974000 6 | ,27.764169375,0.80709375,0.18603175,,1948000 7 | ,27.7461545,0.81010375,0.17828125,,2922000 8 | ,27.81304375,0.8126783125,0.1739021875,,3896000 9 | ,27.778509375,0.8133355625,0.171497375,,4870000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/MipNeRF360/treehill.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,23.5707463888889,0.598731166666667,0.450558222222222,,113100 3 | ,23.7491736111111,0.613477944444444,0.4222035,,188500 4 | Tiny,23.9207421666667,0.6328995,0.382923166666667,,377000 5 | Light,24.0268296666667,0.649493055555555,0.3433625,,754000 6 | ,24.059238,0.662101222222222,0.307301944444444,,1508000 7 | ,24.0576217222222,0.666779055555555,0.290483166666667,,2262000 8 | ,24.0292371666667,0.668893444444445,0.280886722222222,,3016000 9 | ,24.0213101666667,0.671053166666667,0.274060222222222,,3770000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/TanksAndTemples/train.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,23.7384054210526,0.794123394736842,0.290622921052632,,32700 3 | ,23.7305887894737,0.793733868421052,0.290882289473684,,54500 4 | Tiny,23.7188690526316,0.793902315789474,0.291404763157895,,109000 5 | Light,24.6081870526316,0.815546078947368,0.259069868421053,,218000 6 | ,24.9819604736842,0.828847263157895,0.232989447368421,,436000 7 | ,25.0851108684211,0.835408157894737,0.220347421052632,,654000 8 | ,25.2077902105263,0.840017368421052,0.210676368421053,,872000 9 | ,25.1995545,0.842741,0.204792315789474,,1090000 10 | -------------------------------------------------------------------------------- /sources/results_pateux2025bogauss/TanksAndTemples/truck.csv: -------------------------------------------------------------------------------- 1 | Submethod,PSNR,SSIM,LPIPS,Size [Bytes],#Gaussians 2 | ,24.8890003125,0.850925375,0.24387925,,77400 3 | ,25.452209375,0.86399446875,0.22722346875,,129000 4 | Tiny,25.9792948125,0.8802033125,0.19569715625,,258000 5 | Light,26.39089509375,0.8900245,0.17149440625,,516000 6 | ,26.5961739375,0.89608409375,0.15304578125,,1032000 7 | ,26.70768234375,0.89849128125,0.14454084375,,1548000 8 | ,26.73006709375,0.89959284375,0.14023828125,,2064000 9 | ,26.783607125,0.90049896875,0.1358410625,,2580000 10 | --------------------------------------------------------------------------------