├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Jia Zheng 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Scene Understanding [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | 3 | A curated list of awesome scene understanding papers, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision). 4 | 5 | * 📷 Multi-view images 6 | * 🎲 Point cloud 7 | 8 | ## Related Resources 9 | 10 | * [3D Machine Learning](https://github.com/timzhang642/3D-Machine-Learning) 11 | 12 | * [Awesome Holistic 3D](https://github.com/holistic-3d/awesome-holistic-3d) 13 | 14 | * [Awesome Planar Reconstruction](https://github.com/chenzhaiyu/awesome-planar-reconstruction) 15 | 16 | * [Wireframe](https://github.com/Delay-Xili/Wireframe) 17 | 18 | * [Line Segment](https://github.com/lh9171338/Line-Segment-Detection-Papers) 19 | 20 | ## Workshops and Tutorials 21 | 22 | * [Holistic Structures for 3D Vision Workshop at ICCV 2021](https://holistic-3d.github.io/iccv21/) 23 | 24 | * [Holistic Scene Structures for 3D Vision Workshop at ECCV 2020](https://holistic-3d.github.io/eccv20/) 25 | 26 | * [Holistic 3D Reconstruction: Learning to Reconstruct Holistic 3D Structures from Sensorial Data at ICCV 2019](https://holistic-3d.github.io/iccv19/) 27 | 28 | ## Survey 29 | 30 | | Papers | Venue | Links | 31 | |--------|-------|-------| 32 | | [Neural Fields in Robotics: A Survey](https://arxiv.org/abs/2410.20220) | arXiv 2024 | | 33 | | [Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes](https://arxiv.org/abs/2304.03188) | CGF 2023 | | 34 | | [State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments](http://vic.crs4.it/data/papers/eg2020-star-indoor.pdf) | CGF 2020 | [[project]](http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id=%27Pintore:2020:SI3%27) | 35 | | [Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8573760&tag=1) | IEEE Access 2019 | | 36 | | [RGBD Datasets: Past, Present and Future](https://arxiv.org/abs/1604.00999) | CVPR Workshop 2016 | [[project]](http://www.michaelfirman.co.uk/RGBDdatasets/) | 37 | 38 | ## Dataset 39 | 40 | ### Realistic Dataset 41 | 42 | | Papers | Venue | Links | 43 | |--------|-------|-------| 44 | | [ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes](https://arxiv.org/abs/2308.11417) | ICCV 2023 | [[project]](https://kaldir.vc.in.tum.de/scannetpp/) | 45 | | [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://openreview.net/forum?id=tjZjv_qh_CE) | NeurIPS 2021 Dataset Track | [[code]](https://github.com/apple/ARKitScenes) | 46 | | [Zillow Indoor Dataset: Annotated Floor Plans With 360˚ Panoramas and 3D Room Layouts](https://openaccess.thecvf.com/content/CVPR2021/papers/Cruz_Zillow_Indoor_Dataset_Annotated_Floor_Plans_With_360deg_Panoramas_and_CVPR_2021_paper.pdf) | CVPR 2021 | [[code]](https://github.com/zillow/zind) | 47 | | [HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures](https://arxiv.org/abs/2008.03286) | CoRR 2020 | [[project]](https://holicity.io/) | 48 | | [OASIS: A Large-Scale Dataset for Single Image 3D in the Wild](https://arxiv.org/abs/2007.13215) | CVPR 2020 | [[project]](https://oasis.cs.princeton.edu/) | 49 | | [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://arxiv.org/abs/1910.02527) | ICCV 2019 | [[project]](https://3dscenegraph.stanford.edu/) | 50 | | [The Replica Dataset: A Digital Replica of Indoor Spaces](https://arxiv.org/abs/1906.05797) | CoRR 2019 | [[code]](https://github.com/facebookresearch/Replica-Dataset) | 51 | | [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://arxiv.org/abs/1709.06158) | 3DV 2017 | [[project]](https://niessner.github.io/Matterport/) | 52 | | [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://arxiv.org/abs/1702.01105) | CoRR 2017 | [[project]](http://buildingparser.stanford.edu/dataset.html) | 53 | | [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://arxiv.org/abs/1702.04405) | CVPR 2017 | [[project]](http://www.scan-net.org/) | 54 | | [SceneNN: a Scene Meshes Dataset with aNNotations](http://hkust-vgd.ust.hk/scenenn/home/pdf/dataset_3dv16.pdf) | 3DV 2016 | [[project]](http://hkust-vgd.ust.hk/scenenn/home/) | 55 | | [SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite](https://rgbd.cs.princeton.edu/paper.pdf) | CVPR 2015 | [[project]](http://rgbd.cs.princeton.edu/) | 56 | | [SUN3D: A Database of Big Spaces Reconstructed using SfM and Object Labels](http://3dvision.princeton.edu/projects/2013/SUN3D/paper.pdf) | ICCV 2013 | [[project]](http://sun3d.cs.princeton.edu/) | 57 | | [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) | ECCV 2012 | [[project]](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2) | 58 | 59 | ### Synthetic Dataset 60 | 61 | | Papers | Venue | Links | 62 | |--------|-------|-------| 63 | | [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://arxiv.org/abs/2406.11824) | CVPR 2024 | [[project]](https://infinigen.org/) | 64 | | [R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding](https://arxiv.org/abs/2403.12301) | CoRR 2024 | [[project]](https://3dlg-hcvc.github.io/r3ds/) | 65 | | [FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes](https://arxiv.org/abs/2401.03470) | CoRR 2024 | | 66 | | [GeoSynth: A Photorealistic Synthetic Indoor Dataset for Scene Understanding](https://ieeexplore.ieee.org/document/10050341) | VR 2023 | [[code]](https://github.com/geomagical/GeoSynth) | 67 | | [MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis](https://arxiv.org/abs/2107.06149) | CGF 2022 | [[project]](https://coohom.github.io/MINERVAS/) | 68 | | [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://arxiv.org/abs/2011.09127) | ICCV 2021 | [[project]](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) | 69 | | [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://arxiv.org/abs/2011.02523) | ICCV 2021 | [[project]](https://mikeroberts3000.github.io/papers/hypersim) | 70 | | [OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets](https://arxiv.org/abs/2007.12868) | CVPR 2021 | [[project]](https://ucsd-openrooms.github.io/) | 71 | | [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://arxiv.org/abs/1908.00222) | ECCV 2020 | [[project]](https://structured3d-dataset.org) | 72 | | [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](https://arxiv.org/abs/1809.00716) | BMVC 2018 | [[project]](https://interiornet.org/) | 73 | | [SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?](https://arxiv.org/abs/1612.05079) | ICCV 2017 | [[project]](https://robotvault.bitbucket.io/scenenet-rgbd.html) | 74 | | [Semantic Scene Completion from a Single Depth Image](https://arxiv.org/abs/1611.08974) | CVPR 2017 | | 75 | | [SceneNet: Understanding Real World Indoor Scenes With Synthetic Data](http://arxiv.org/abs/1511.07041) | CVPR 2016 | [[project]](https://robotvault.bitbucket.io/) | 76 | | [The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes](https://openaccess.thecvf.com/content_cvpr_2016/html/Ros_The_SYNTHIA_Dataset_CVPR_2016_paper.html) | CVPR 2016 | [[project]](http://synthia-dataset.net/) | 77 | 78 | ## Holistic Scene Understanding 79 | 80 | ### Perspective Image 81 | 82 | | Papers | Venue | Links | 83 | |--------|-------|-------| 84 | | [Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture](https://arxiv.org/abs/2311.00457) | 3DV 2024 | [[project]](https://dali-jack.github.io/SSR) | 85 | | [Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes](https://arxiv.org/abs/2207.08656) | ECCV 2022 | [[code]](https://github.com/UncleMEDM/InstPIFu) | 86 | | [Holistic 3D Scene Understanding from a Single Image with Implicit Representation](https://arxiv.org/abs/2103.06422) | CVPR 2021 | [[project]](https://chengzhag.github.io/publication/im3d/) [[code]](https://github.com/chengzhag/Implicit3DUnderstanding) | 87 | | [Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image](https://arxiv.org/abs/2002.12212) | CVPR 2020 | [[code]](https://github.com/yinyunie/Total3DUnderstanding) | 88 | | [PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points](https://arxiv.org/abs/1912.07744) | NeurIPS 2019 | | 89 | | [Hoilistc++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense](https://arxiv.org/abs/1909.01507) | ICCV 2019 | [[project]](https://yixchen.github.io/holisticpp/) [[code]](https://github.com/yixchen/holistic_scene_human) | 90 | | [Complete 3D Scene Parsing from an RGBD Image](https://arxiv.org/abs/1710.09490) | IJCV 2018 | | 91 | | [Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation](https://arxiv.org/abs/1810.13049) | NeurIPS 2018 | [[project]](http://siyuanhuang.com/cooperative_parsing/main.html) [[code]](https://github.com/thusiyuan/cooperative_scene_parsing) | 92 | | [Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image](https://arxiv.org/abs/1808.02201) | ECCV 2018 | [[project]](http://siyuanhuang.com/holistic_parsing/main.html) [[code]](https://github.com/thusiyuan/holistic_scene_parsing) | 93 | | [Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene](https://arxiv.org/abs/1712.01812) | CVPR 2018 | [[project]](https://shubhtuls.github.io/factored3d/) [[code]](https://github.com/shubhtuls/factored3d) | 94 | | [Im2CAD](https://arxiv.org/abs/1608.05137) | CVPR 2018 | [[project]](https://homes.cs.washington.edu/~izadinia/im2cad.html) | 95 | | [DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding](https://arxiv.org/abs/1603.04922) | ICCV 2017 | [[project]](http://deepcontext.cs.princeton.edu) | 96 | | [Emptying, Refurnishing, and Relighting Indoor Spaces](https://grail.cs.washington.edu/projects/emptying/emptying.pdf) | SIGGRAPH Asia 2016 | [[project]](https://grail.cs.washington.edu/projects/emptying/) | 97 | | [Scene Parsing by Integrating Function, Geometry and Appearance Models](http://openaccess.thecvf.com/content_cvpr_2013/papers/Zhao_Scene_Parsing_by_2013_CVPR_paper.pdf) | CVPR 2013 | | 98 | | [Understanding Indoor Scenes using 3D Geometric Phrases](http://openaccess.thecvf.com/content_cvpr_2013/papers/Choi_Understanding_Indoor_Scenes_2013_CVPR_paper.pdf) | (CVPR 2013) | | 99 | | [Recovering Free Space of Indoor Scenes from a Single Image](http://vision.cs.uiuc.edu/~vhedau2/Publications/cvpr2012_freespace.pdf) | CVPR 2012 | | 100 | | [Efficient Exact Inference for 3D Indoor Scene Understanding](http://schwingag.de/papers/SchwingEtAl_ECCV2012.pdf) | ECCV 2012 | | 101 | | [Efficient Structured Prediction for 3D Indoor Scene Understanding](https://ttic.uchicago.edu/~rurtasun/publications/schwing_et_al_cvpr12.pdf) | CVPR 2012 | | 102 | | [Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces](https://papers.NeurIPS.cc/paper/4120-estimating-spatial-layout-of-rooms-using-volumetric-reasoning-about-objects-and-surfaces.pdf) | NeurIPS 2010 | | 103 | | [Thinking Inside the Box: Using Appearance Models and Context Based on Room Geometry](http://vision.cs.uiuc.edu/~vhedau2/Publications/eccv2010_think_inside.pdf) | ECCV 2010 | | 104 | 105 | ### Panoramic Image 106 | 107 | | Papers | Venue | Links | 108 | |--------|-------|-------| 109 | | [PanoContext-Former: Panoramic Total Scene Understanding with a Transformer](https://arxiv.org/abs/2305.12497) | CVPR 2024 | | 110 | | [PanelNet: Understanding 360 Indoor Environment via Panel Representation](https://arxiv.org/abs/2305.09078) | CVPR 2023 | | 111 | | [DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization](https://arxiv.org/abs/2108.10743) | ICCV 2021 | [[code]](https://github.com/chengzhag/DeepPanoContext) | 112 | | [HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features](https://arxiv.org/abs/2011.11498) | CVPR 2021 | [[Code]](https://github.com/sunset1995/HoHoNet) | 113 | | [Automatic 3D Indoor Scene Modeling from Single Panorama](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Automatic_3D_Indoor_CVPR_2018_paper.pdf) | CVPR 2018 | | 114 | | [Pano2CAD: Room Layout From A Single Panorama Image](http://bjornstenger.github.io/papers/xu_wacv2017.pdf) | WACV 2017 | | 115 | | [PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding](http://panocontext.cs.princeton.edu/paper.pdf) | ECCV 2014 | [[project]](http://panocontext.cs.princeton.edu/) | 116 | 117 | ## Room Layout Estimation 118 | 119 | ### Perspective Image 120 | 121 | (AW: Atlanta-world, SS: single-floor and single-ceiling, PP: Piece-wise Planarity.) 122 | 123 | | Dataset | Year | Modality | #Frames | Prior | Source | 124 | |---------|------|----------|---------|-------|--------| 125 | | [CAD-Estate][cad-estate] | 2023 | RGB Video | | Generic | RealEstate-10K | 126 | | [Matterport3D-Layout][Matterport3D-Layout] | 2020 | RGB-D | 7360 | PP | Matterport | 127 | | [ScanNet-Layout][ScanNet-Layout] | 2020 | RGB-D | 293 | PP | ScanNet | 128 | | Structured3D | 2020 | RGB-D | 82027 | AW+SS | Structured3D | 129 | | LSUN Room Layout | 2016 | RGB | 5394 | Cuboid | SUN | 130 | | SUN RGB-D | 2015 | RGB-D | 10335 | AW+SS | NYUv2, Berkeley B3DO, and SUN3D | 131 | | NYUv2 303 | 2013 | RGB-D | 303 | Cuboid | NYUv2 | 132 | | Hedau | 2009 | RGB | 366 | Cuboid | | 133 | 134 | | Papers | Venue | Links | 135 | |--------|-------|-------| 136 | | 📷 [PixCuboid: Room Layout Estimation from Multi-view Featuremetric Alignment](https://arxiv.org/abs/2508.04659) | ICCV Workshop 2025 | [[project]](https://ghanning.github.io/PixCuboid/) | 137 | | 📷 [Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model](https://arxiv.org/abs/2502.16779) | ICLR 2025 | [[code]](https://github.com/justacar/Plane-DUSt3R) | 138 | | [Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes](https://arxiv.org/abs/2306.12203) | ICCV Workshop 2023 | [[code]](https://github.com/DavidGillsjo/polygon-HGT) | 139 | | [ST-RoomNet: Learning Room Layout Estimation From Single Image Through Unsupervised Spatial Transformations](https://openaccess.thecvf.com/content/CVPR2023W/VOCVALC/html/Ibrahem_ST-RoomNet_Learning_Room_Layout_Estimation_From_Single_Image_Through_Unsupervised_CVPRW_2023_paper) | CVPR Workshop 2023 | | 140 | | [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://arxiv.org/abs/2104.07986) | WACV 2022 | [[code]](https://github.com/CYang0515/NonCuboidRoom) | 141 | | [RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View](https://arxiv.org/abs/2110.00644) | CoRR 2021 | | 142 | | [GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes](https://arxiv.org/abs/2008.06286) | ECCV 2020 | [[Matterport3D Layout Dataset]][Matterport3D-Layout] 143 | | [Structural Deep Metric Learning for Room Layout Estimation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630715.pdf) | ECCV 2020 | | 144 | | [General 3D Room Layout from a Single View by Render-and-Compare](http://arxiv.org/abs/2001.02149) | ECCV 2020 | [[project]](https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/general-3d-room-layout-from-a-single-view-by-render-and-compare/) [[ScanNet-Layout Dataset]][ScanNet-Layout] [[code]](https://github.com/vevenom/RoomLayout3D_RandC) | 145 | | [Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation](https://arxiv.org/abs/1910.12257) | WACV 2020 | | 146 | | [Flat2Layout: Flat Representation for Estimating Layout of General Room Types](https://arxiv.org/abs/1905.12571) | CoRR 2019 | | 147 | | [Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts](https://arxiv.org/abs/1905.03105) | ACCV 2018 | | 148 | | [RoomNet: End-to-End Room Layout Estimation](https://arxiv.org/abs/1703.06241) | ICCV 2017 | | 149 | | [Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation](https://arxiv.org/abs/1707.00383) | CVPR 2017 | [[project]](https://sites.google.com/view/st-pio/) | 150 | | [A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method](https://arxiv.org/abs/1607.00598) | ACCV 2016 | | 151 | | [DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes](http://openaccess.thecvf.com/content_cvpr_2016/papers/Dasgupta_DeLay_Robust_Spatial_CVPR_2016_paper.pdf) | CVPR 2016 | | 152 | | [Learning Informative Edge Maps for Indoor Scene Layout Prediction](http://openaccess.thecvf.com/content_iccv_2015/papers/Mallya_Learning_Informative_Edge_ICCV_2015_paper.pdf) | ICCV 2015 | | 153 | | [Rent3D: Floor-Plan Priors for Monocular Layout Estimation](http://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Rent3D_Floor-Plan_Priors_2015_CVPR_paper.pdf) | CVPR 2015 | [[project]](http://www.cs.toronto.edu/~fidler/projects/rent3D.html) | | 154 | | [Box In the Box: Joint 3D Layout and Object Reasoning from Single Images](http://openaccess.thecvf.com/content_iccv_2013/papers/Schwing_Box_in_the_2013_ICCV_paper.pdf) | CVPR 2013 | | 155 | | [Estimating the 3D Layout of Indoor Scenes and its Clutter from Depth Sensors](http://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Estimating_the_3D_2013_ICCV_paper.pdf) | ICCV 2013 | [[project]](https://cs.stanford.edu/people/zjian/project/ICCV13DepthLayout/ICCV13DepthLayout.html) | 156 | | [Recovering the Spatial Layout of Cluttered Rooms](http://dhoiem.cs.illinois.edu/publications/iccv2009_hedau_indoor.pdf) | ICCV 2009 | | 157 | 158 | ### Panoramic Image 159 | 160 | (MW: Manhattan world, AW: Atlanta world, SS: single-floor and single-ceiling.) 161 | 162 | | Dataset | Year | Modality | #Frames | Prior | Source | 163 | |---------|------|----------|---------|-------|--------| 164 | | [ZInD][ZInD] | 2021 | RGB | 71474 | AW+SS | ZinD | 165 | | [MatterportLayout][MatterportLayout] | 2020 | RGB-D | 2295 | MW+SS | Matterport | 166 | | Structured3D | 2020 | RGB-D | 196515 | AW+SS | Structured3D | 167 | | [LayoutMP3D][LayoutMP3D] | 2020 | RGB-D | 2505 | MW+SS | Matterport | 168 | | 2D-3D-S | 2018 | RGB-D | 571 | Cuboid | 2D-3D-S | 169 | | PanoContext | 2014 | RGB | 500 | Cuboid | SUN360 | 170 | 171 | | Papers | Venue | Links | 172 | |--------|-------|-------| 173 | | [uLayout: Unified Room Layout Estimation for Perspective and Panoramic Images](https://openaccess.thecvf.com//content/WACV2025/html/Lee_uLayout_Unified_Room_Layout_Estimation_for_Perspective_and_Panoramic_Images_WACV_2025_paper.html) | WACV 2025 | | 174 | | [No More Ambiguity in 360◦ Room Layout via Bi-Layout Estimation](https://www.amazon.science/publications/no-more-ambiguity-in-360-room-layout-via-bi-layout-estimation) | CVPR 2024 | | 175 | | [Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction](https://arxiv.org/abs/2311.18695) | CVPR 2024 | | 176 | | [iBARLE: imBalance-Aware Room Layout Estimation](https://arxiv.org/abs/2308.15050) | CoRR 2023 | | 177 | | 📷 [GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network](https://arxiv.org/abs/2210.11419) | CVPR Workshop 2023 | | 178 | | [Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs](https://arxiv.org/abs/2304.12624) | CVPR Workshop 2023 | | 179 | | [U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation](https://arxiv.org/abs/2304.08580) | CVPR 2023 | | 180 | | [Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness](https://arxiv.org/abs/2303.00971) | CVPR 2023 | [[Code](https://github.com/zhijieshen-bjtu/DOPNet)] | 181 | | 📷 [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://arxiv.org/abs/2210.12935) | NeurIPS 2022 | [[Project]](https://enriquesolarte.github.io/360-mlc/) | 182 | | [3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform](https://arxiv.org/abs/2207.09291) | ECCV 2022 | [[Code]](https://github.com/Starrah/DMH-Net) | 183 | | [3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds](https://openaccess.thecvf.com/content/CVPR2022W/OmniCV/papers/Jia_3D_Room_Layout_Recovery_Generalizing_Across_Manhattan_and_Non-Manhattan_Worlds_CVPRW_2022_paper.pdf) | CVPR 2022 | | 184 | | 📷 [PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation](https://arxiv.org/abs/2203.15965) | CVPR 2022 | [[code]](https://github.com/zillow/psmnet-layout) | 185 | | [Self-supervised 360˚ Room Layout Estimation](https://arxiv.org/abs/2203.16057) | CoRR 2022 | [[code]](https://github.com/joshua049/Stereo-360-Layout) 186 | | [LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network](https://arxiv.org/abs/2203.01824) | CVPR 2022 | | 187 | | [Deep3DLayout: 3D Reconstruction of an Indoor Layout from a Spherical Panoramic Image](http://publications.crs4.it/pubdocs/2021/PAAG21/sigasia2021-deep3dlayout.pdf) | SIGGRAPH Asia 2021 | [[project]](http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id=%27Pintore:2021:D3R%27) | 188 | | [Transferable End-to-end Room Layout Estimation via Implicit Encoding](https://arxiv.org/abs/2112.11340) | CoRR 2021 | [[project]](https://sites.google.com/view/transferrl/) 189 | | [OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas](https://arxiv.org/abs/2104.09403) | CVPR Workshop 2021 | [[code]](https://github.com/rshivansh/OmniLayout) 190 | | [LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering](https://arxiv.org/abs/2104.00568) | CVPR 2021 | [[project]](https://fuenwang.ml/project/led2net/) [[code]](https://github.com/fuenwang/LED2-Net) 191 | | [SSLayout360: Semi-Supervised Indoor Layout Estimation from 360 Panorama](https://arxiv.org/abs/2103.13696) | CVPR 2021 | | 192 | | [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://arxiv.org/abs/2102.03939) | Image and Vision Computing 2021 | [[project]](https://vcl3d.github.io/SingleShotCuboids/) [[code]](https://github.com/VCL3D/SingleShotCuboids) 193 | | [Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods](https://arxiv.org/abs/1910.04099) | IJCV 2021 | [[code]](https://github.com/zouchuhang/LayoutNetv2) [[MatterportLayout Dataset]][MatterportLayout] 194 | | [Training and Post Processing 3D Room Layout Beyond the Manhattan World Assumption](https://arxiv.org/abs/2009.02857) | ECCV Workshop 2020 | | 195 | | [Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610647.pdf) | ECCV 2020 | | 196 | | [AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image Beyond the Manhattan World Assumption](http://vic.crs4.it/data/papers/eccv2020-atlantanet.pdf) | ECCV 2020 | [[project]](https://www.crs4.it/vic/cgi-bin/bib-page.cgi?id=%27Pintore:2020:AI3%27) [[code]](https://github.com/crs4/AtlantaNet) 197 | | [Corners for Layout: End-to-End Layout Recovery from 360 Images](https://arxiv.org/abs/1903.08094) | ICRA 2019 | [[project]](https://cfernandezlab.github.io/CFL/) [[code]](https://github.com/cfernandezlab/CFL-End-to-End-Layout-Recovery-from-360-Images) 198 | | [DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama](https://arxiv.org/abs/1811.11977) | CVPR 2019 | [[project]](https://cgv.cs.nthu.edu.tw/projects/dulanet) 199 | | [HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation](https://arxiv.org/abs/1901.03861) | CVPR 2019 | [[code]](https://github.com/sunset1995/HorizonNet) 200 | | [Layouts from Panoramic Images with Geometry and Deep Learning](https://arxiv.org/abs/1806.08294) | IROS 2018 | [[code]](https://github.com/cfernandezlab/Lines-and-Vanishing-Points-directly-on-Panoramas) 201 | | [LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image](https://arxiv.org/abs/1803.08999) | (CVPR 2018) | [[code]](https://github.com/zouchuhang/LayoutNet) 202 | | [Efficient 3D Room Shape Recovery From a Single Panorama](http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Efficient_3D_Room_CVPR_2016_paper.pdf) | CVPR 2016 | [[code]](https://github.com/YANG-H/Panoramix) 203 | 204 | ## Floorplan 205 | 206 | | Papers | Venue | Links | 207 | |--------|-------|-------| 208 | | 📷 [NadirFloorNet: reconstructing multi-room floorplans from a small set of registered panoramic images](https://openaccess.thecvf.com/content/CVPR2025W/USM3D/papers/Pintore_NadirFloorNet_reconstructing_multi-room_floorplans_from_a_small_set_of_registered_CVPRW_2025_paper.pdf) | CVPRW 2025 | | 209 | | 🎲 [FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation](https://arxiv.org/abs/2407.10687) | ECCV 2024 | [[code]](https://github.com/Daisy-1227/FRI-Net) | 210 | | 🎲 [PolyRoom: Room-aware Transformer for Floorplan Reconstruction](https://arxiv.org/abs/2407.10439) | ECCV 2024 | [[code]](https://github.com/3dv-casia/PolyRoom/) | 211 | | 🎲 [PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models](https://arxiv.org/abs/2306.01461) | NeurIPS 2023 | [[project]](https://poly-diffuse.github.io/) | 212 | | 🎲 [Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries](https://arxiv.org/abs/2211.15658) | CVPR 2023 | [[project]](https://ywyue.github.io/RoomFormer/) [[code]](https://github.com/ywyue/RoomFormer) | 213 | | 📷 [Floorplan Restoration by Structure Hallucinating Transformer Cascades](https://arxiv.org/abs/2206.00645) | CoRR 2022 | | 214 | | 📷 [MVLayoutNet: 3D Layout Reconstruction with Multi-View Panoramas](https://arxiv.org/abs/2112.06133) | CoRR 2021 | | 215 | | 📷 [Extreme Structure From Motion for Indoor Panoramas Without Visual Overlaps](https://openaccess.thecvf.com/content/ICCV2021/papers/Shabani_Extreme_Structure_From_Motion_for_Indoor_Panoramas_Without_Visual_Overlaps_ICCV_2021_paper.pdf) | ICCV 2021 | [[code]](https://github.com/aminshabani/extreme-indoor-sfm) | 216 | | 🎲 [MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans](https://arxiv.org/abs/2103.11161) | ICCV 2021 | | 217 | | 🎲 [Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes](http://arxiv.org/abs/2003.07356) | CoRR 2020 | | 218 | | 🎲 [Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path](https://arxiv.org/abs/1908.06702) | ICCV 2019 | [[project]](https://jcchen.me/floor-sp/) [[code]](https://github.com/woodfrog/floor-sp) | 219 | | 📷 [Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans](https://arxiv.org/abs/1812.06677) | ICCV 2019 | [[project]](https://enigma-li.github.io/projects/indoorRecons/floorplanJigsaw.html) | 220 | | 🎲 [DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences](https://arxiv.org/abs/1904.11595) | CoRR 2019 | | 221 | | 📷 [FloorNet: A unified framework for floorplan reconstruction from 3D scans](https://arxiv.org/abs/1804.00090) | ECCV 2018 | [[project]](http://art-programmer.github.io/floornet.html) [[code]](https://github.com/art-programmer/FloorNet) | 222 | 223 | ### Floorplan Vectorization 224 | 225 | | Papers | Venue | Links | 226 | |--------|-------|-------| 227 | | [VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.pdf) | CVPR 2023 | [[code]](https://github.com/DrZiji/VecFloorSeg) | 228 | | [Parsing Line Segments of Floor Plan Images Using Graph Neural Networks](https://arxiv.org/abs/2303.03851) | CoRR 2023 | | 229 | | [Residential floor plan recognition and reconstruction](https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.pdf) | CVPR 2021 | | 230 | | [Versailles-FP dataset: Wall Detection in Ancient Floor Plans](https://arxiv.org/abs/2103.08064) | CoRR 2021 | | 231 | | [Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention](https://arxiv.org/abs/1908.11025) | ICCV 2019 | [[project]](https://github.com/zlzeng/DeepFloorplan) | 232 | | [CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis](https://arxiv.org/abs/1904.01920) | Scandinavian Conference on Image Analysis 2019 | [[code]](https://github.com/CubiCasa/CubiCasa5k) | 233 | | [Raster-to-Vector: Revisiting Floorplan Transformation](http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Raster-To-Vector_Revisiting_Floorplan_ICCV_2017_paper.pdf) | ICCV 2017 | [[project]](http://art-programmer.github.io/floorplan-transformation.html) [[code]](https://github.com/art-programmer/FloorplanTransformation) | 234 | 235 | ### Visual Localization 236 | 237 | | Papers | Venue | Links | 238 | |--------|-------|-------| 239 | | [SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments](https://arxiv.org/abs/2404.10527) | ECCV 2024 | [[project]](https://fraunhoferhhi.github.io/spvloc/) [[code]](https://github.com/fraunhoferhhi/spvloc) | 240 | | [LaLaLoc++: Global Floor Plan Comprehension for Layout Localisation in Unvisited Environments](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136870681.pdf) | ECCV 2022 | [[code]](https://github.com/ActiveVisionLab/LaLaLoc) | 241 | | [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://arxiv.org/abs/2204.00157) | CVPR 2022 | | 242 | | [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://arxiv.org/abs/2104.09169) | ICCV 2021 | | 243 | 244 | 245 | ## Primitive 246 | 247 | ### Junction 248 | 249 | | Papers | Venue | Links | 250 | |--------|-------|-------| 251 | | [Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes](https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Ramalingam_Manhattan_Junction_Catalogue_2013_CVPR_paper.pdf) | CVPR 2013 | | 252 | 253 | ### Line Segment and Wireframe 254 | 255 | | Papers | Venue | Links | 256 | |--------|-------|-------| 257 | | [ScaleLSD: Scalable Deep Line Segment Detection Streamlined](https://arxiv.org/abs/2506.09369) | CVPR 2025 | [[project](https://ant-research.github.io/scalelsd/)] | 258 | | 📷[Volumetric Wireframe Parsing from Neural Attraction Fields](https://arxiv.org/abs/2307.10206) | CoRR 2023 | [[code](https://github.com/cherubicXN/neat)] | 259 | | 📷[NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images](https://arxiv.org/abs/2303.07653) | CVPR 2023 | [[project]](https://yunfan1202.github.io/NEF/) | 260 | | [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://arxiv.org/abs/2212.07766) | CoRR 2022 | [[Code]](https://github.com/cvg/DeepLSD) | 261 | | [Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning](https://arxiv.org/abs/2210.12971) | CoRR 2022 | | 262 | | 🎲[Learning to Construct 3D Building Wireframes from 3D Line Clouds](https://arxiv.org/abs/2208.11948) | BMVC 2022 | [[Code]](https://github.com/Luo1Cheng/LC2WF) | 263 | | [HoW-3D: Holistic 3D Wireframe Perception from a Single Image](https://arxiv.org/abs/2208.06999) | 3DV 2022 | [[Code]](https://github.com/Wenchao-M/HoW-3D) | 264 | | [Semantic Room Wireframe Detection from a Single View](https://arxiv.org/abs/2206.00491) | ICPR 2022 | [[code]](https://github.com/DavidGillsjo/SRW-Net) | 265 | | [Towards Real-time and Light-weight Line Segment Detection](https://arxiv.org/abs/2106.00186) | AAAI 2022 | [[code]](https://github.com/navervision/mlsd) | 266 | | [Hole-robust Wireframe Detection](https://arxiv.org/abs/2111.15064) | WACV 2022 | | 267 | | [Fully Convolutional Line Parsing](https://arxiv.org/abs/2104.11207) | Neurocomputing 2022 | [[code]](https://github.com/Delay-Xili/F-Clip) | 268 | | [ELSD: Efficient Line Segment Detector and Descriptor](https://arxiv.org/abs/2104.14205) | ICCV 2021 | | 269 | | [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://arxiv.org/abs/2104.03362) | CVPR 2021 | [[code]](https://github.com/cvg/SOLD2) | 270 | | [Line Segment Detection Using Transformers without Edges](https://arxiv.org/abs/2101.01909) | CVPR 2021 | [[code]](https://github.com/mlpc-ucsd/LETR/) | 271 | | [PlueckerNet: Learn to Register 3D Line Reconstructions](https://arxiv.org/abs/2012.01096) | CVPR 2020 | [[code]](https://github.com/Liumouliu/PlueckerNet) | 272 | | [LGNN: A Context-aware Line Segment Detector](https://arxiv.org/abs/2008.05892) | ACM MM 2020 | | 273 | | [TP-LSD: Tri-Points Based Line Segment Detector](https://arxiv.org/abs/2009.05505) | ECCV 2020 | [[code]](https://github.com/Siyuada7/TP-LSD) | 274 | | [Deep Hough-Transform Line Priors](https://arxiv.org/abs/2007.09493) | ECCV 2020 | [[code]](https://github.com/yanconglin/Deep-Hough-Transform-Line-Priors) | 275 | | [Deep Hough Transform for Semantic Line Detection](https://arxiv.org/abs/2003.04676) | ECCV 2020 | [[code]](https://github.com/Hanqer/deep-hough-transform) | 276 | | [Holistically-Attracted Wireframe Parsing](https://arxiv.org/abs/2003.01663) | CVPR 2020 | [[code]](https://github.com/cherubicXN/hawp) | 277 | | [Learning to Reconstruct 3D Manhattan Wireframes from a Single Image](https://arxiv.org/abs/1905.07482) | ICCV 2019 | [[code]](https://github.com/zhou13/shapeunity) | 278 | | [End-to-End Wireframe Parsing](https://arxiv.org/abs/1905.03246) | ICCV 2019 | [[code]](https://github.com/zhou13/lcnn) | 279 | | [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://arxiv.org/abs/1905.03415) | CVPR 2019 | [[code]](https://github.com/svip-lab/PPGNet) | 280 | | [Learning Attraction Field Representation for Robust Line Segment Detection](https://arxiv.org/abs/1812.02122) | CVPR 2019 | [[code]](https://github.com/cherubicXN/afm_cvpr2019) | 281 | | [Novel Single View Constraints for Manhattan 3D Line Reconstruction](https://arxiv.org/abs/1810.03737) | 3DV 2018 | | 282 | | [Learning to Parse Wireframes in Images of Man-Made Environments](http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Learning_to_Parse_CVPR_2018_paper.pdf) | CVPR 2018 | [[code]](https://github.com/huangkuns/wireframe) | 283 | | [A Novel Linelet-Based Representation for Line Segment Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7926451) | TPAMI 2018 | | 284 | | [MCMLSD: A Dynamic Programming Approach to Line Segment Detection](http://openaccess.thecvf.com/content_cvpr_2017/papers/Almazan_MCMLSD_A_Dynamic_CVPR_2017_paper.pdf) | CVPR 2017 | | 285 | | [Lifting 3D Manhattan Lines from a Single Image](https://openaccess.thecvf.com/content_iccv_2013/papers/Ramalingam_Lifting_3D_Manhattan_2013_ICCV_paper.pdf) | ICCV 2013 | | 286 | | [LSD: A Fast Line Segment Detector with a False Detection Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4731268) | TPAMI 2010 | | 287 | 288 | ### Outdoor Architecture 289 | 290 | | Papers | Venue | Links | 291 | |--------|-------|-------| 292 | | [HEAT: Holistic Edge Attention Transformer for Structured Reconstruction](https://arxiv.org/abs/2111.15143) | CVPR 2022 | [[Project]](https://heat-structured-reconstruction.github.io/) | 293 | | [Structured Outdoor Architecture Reconsruction by Exploration and Classification](https://arxiv.org/abs/2108.07990) | ICCV 2021 | [[Project]](https://zhangfuyang.github.io/expcls/) | 294 | | [Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses](https://arxiv.org/abs/2012.09340) | CVPR 2021 | [[Code]](https://github.com/yi-ming-qian/roofgan) | 295 | | [Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Inference](https://arxiv.org/abs/1912.05135) | ECCV 2020 | [[Project]](https://ennauata.github.io/buildings2vec/page.html) | 296 | | [Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction](https://arxiv.org/abs/1912.01756) | CVPR 2020 | [[Project]](https://zhangfuyang.github.io/convmpn/) | 297 | 298 | ### Plane 299 | 300 | | Papers | Venue | Links | 301 | |--------|-------|-------| 302 | | [Towards In-the-wild 3D Plane Reconstruction from a Single Image](https://openaccess.thecvf.com//content/CVPR2025/html/Liu_Towards_In-the-wild_3D_Plane_Reconstruction_from_a_Single_Image_CVPR_2025_paper) | CVPR 2025 | [[code]](https://github.com/jcliu0428/ZeroPlane) | 303 | | [MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction](https://arxiv.org/abs/2411.01226) | IROS 2024 | [[code]](https://github.com/thuzhaowang/MonoPlane) | 304 | | 📷 [UniPlane: Unified Plane Detection and Reconstruction from Posed Monocular Videos](https://arxiv.org/abs/2407.03594) | CoRR 2024 | | 305 | | 📷 [AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings](https://arxiv.org/abs/2406.08960) | CVPR 2024 | [[project]](https://nianticlabs.github.io/airplanes/) | 306 | | [PlaneRecTR: Unified Query learning for 3D Plane Recovery from a Single View](https://arxiv.org/abs/2307.13756) | ICCV 2023 | [[Code]](https://github.com/SJingjia/PlaneRecTR) | 307 | | 📷 [NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction](https://arxiv.org/abs/2211.16799) | CoRR 2022 | [[Code]](https://github.com/IceTTTb/NopeSAC) | 308 | | 📷 [PlaneFormers: From Sparse View Planes to 3D Reconstruction](https://arxiv.org/abs/2208.04307) | ECCV 2022 | [[project]](https://samiragarwala.github.io/PlaneFormers) [[code]](https://github.com/samiragarwala/PlaneFormers) 309 | | 📷 [PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos](https://arxiv.org/abs/2206.07710) | CVPR 2022 | [[Project]](https://neu-vi.github.io/planarrecon/) | 310 | | [PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image](https://arxiv.org/abs/2110.11219) | BMVC 2021 | [[code]](https://github.com/EryiXie/PlaneRecNet) | 311 | | [PlaneTR: Structure-Guided Transformers for 3D Plane Recovery](https://arxiv.org/abs/2107.13108) | ICCV 2021 | [[code]](https://github.com/IceTTTb/PlaneTR3D) | 312 | | 📷 [Planar Surface Reconstruction From Sparse Views](https://arxiv.org/abs/2103.14644) | ICCV 2021 | [[project]](https://jinlinyi.github.io/SparsePlanes/) [[code]](https://github.com/jinlinyi/SparsePlanes) 313 | | [Indoor Panorama Planar 3D Reconstruction via Divide and Conquer](https://arxiv.org/abs/2106.14166) | CVPR 2021 | [[code]](https://github.com/sunset1995/PanoPlane360) | 314 | | [Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520324.pdf) | ECCV 2020 | [[code]](https://github.com/yi-ming-qian/interplane) | 315 | | [Peek-a-Boo: Occlusion Reasoning in Indoor Scenes with Plane Representations](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Peek-a-Boo_Occlusion_Reasoning_in_Indoor_Scenes_With_Plane_Representations_CVPR_2020_paper.pdf) | CVPR 2020 | [[project]](https://www.nec-labs.com/~mas/peekaboo/) | 316 | | [Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding](https://arxiv.org/abs/1902.09777) | CVPR 2019 | [[code]](https://github.com/svip-lab/PlanarReconstruction) | 317 | | [PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image](https://arxiv.org/abs/1812.04072) | CVPR 2019 | [[project]](https://research.nvidia.com/publication/2019-06_PlaneRCNN) [[code]](https://github.com/NVlabs/planercnn) | 318 | | [Recovering 3D Planes from a Single Image via Convolutional Neural Networks](http://openaccess.thecvf.com/content_ECCV_2018/papers/Fengting_Yang_Recovering_3D_Planes_ECCV_2018_paper.pdf) | ECCV 2018 | [[code]](https://github.com/fuy34/planerecover) | 319 | | [PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image](https://arxiv.org/abs/1804.06278) | CVPR 2018 | [[project]](https://www.cse.wustl.edu/~chenliu/planenet.html) [[code]](https://github.com/art-programmer/PlaneNet) | 320 | 321 | ## Vanishing Point 322 | 323 | | Papers | Venue | Links | 324 | |--------|-------|-------| 325 | | [Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction](https://arxiv.org/abs/2308.10694) | ICCV 2023 | [[code]](https://github.com/cvg/VP-Estimation-with-Prior-Gravity) | 326 | | [Transformer Based Line Segment Classifier with Image Context for Real-Time Vanishing Point Detection in Manhattan World](https://openaccess.thecvf.com/content/CVPR2022/papers/Tong_Transformer_Based_Line_Segment_Classifier_With_Image_Context_for_Real-Time_CVPR_2022_paper.pdf) | CVPR 2022 | | 327 | | [Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish](https://arxiv.org/abs/2203.08586) | CVPR 2022 | | 328 | | [VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_VaPiD_A_Rapid_Vanishing_Point_Detector_via_Learned_Optimizers_ICCV_2021_paper.pdf) | ICCV 2021 | | 329 | | [NeurVPS: Neural Vanishing Point Scanning via Conic Convolution](https://arxiv.org/abs/1910.06316) | NeurIPS 2021 | [[Code]](https://github.com/zhou13/neurvps) | 330 | 331 | [ScanNet-Layout]: https://github.com/vevenom/ScanNet-Layout 332 | [Matterport3D-Layout]: https://vsislab.github.io/Matterport3D-Layout/ 333 | [MatterportLayout]: https://github.com/ericsujw/Matterport3DLayoutAnnotation 334 | [LayoutMP3D]: https://github.com/fuenwang/LayoutMP3D 335 | [ZInD]: https://github.com/zillow/zind 336 | [cad-estate]: https://github.com/google-research/cad-estate 337 | --------------------------------------------------------------------------------