├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | This is free and unencumbered software released into the public domain. 2 | 3 | Anyone is free to copy, modify, publish, use, compile, sell, or 4 | distribute this software, either in source code form or as a compiled 5 | binary, for any purpose, commercial or non-commercial, and by any 6 | means. 7 | 8 | In jurisdictions that recognize copyright laws, the author or authors 9 | of this software dedicate any and all copyright interest in the 10 | software to the public domain. We make this dedication for the benefit 11 | of the public at large and to the detriment of our heirs and 12 | successors. We intend this dedication to be an overt act of 13 | relinquishment in perpetuity of all present and future rights to this 14 | software under copyright law. 15 | 16 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 17 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 18 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 19 | IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR 20 | OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, 21 | ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR 22 | OTHER DEALINGS IN THE SOFTWARE. 23 | 24 | For more information, please refer to 25 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 3D-Reconstruction-with-Deep-Learning-Methods 2 | 3 | The focus of this list is on open-source projects hosted on Github. 4 | 5 | **Projects released on Github** 6 | 7 | | TITLE | KEYWORDS | URL | LICENSE | Awesomeness | 8 | | ------------------------------------------------------------ | --------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----------- | 9 | | High Quality Monocular Depth Estimation via Transfer Learning | TensorFlow, PyTorch | https://github.com/ialhashim/DenseDepth https://arxiv.org/abs/1812.11941 | GPL-3.0 | | 10 | | Multi-view stereo image-based 3D reconstruction | | https://github.com/adahbingee/pais-mvs | nn | | 11 | | Hybrid Ensemble Approach For 3D Object Reconstruction from Multi-View Monocular RGB images | | https://github.com/Ajithbalakrishnan/3D-Object-Reconstruction-from-Multi-View-Monocular-RGB-images | nn | | 12 | | Deep 3D Semantic Scene Extrapolation | hybrid CNN, GAN, TensorFlow | https://github.com/AliAbbasi/Deep-3D-Semantic-Scene-Extrapolation http://user.ceng.metu.edu.tr/~ys/pubs/extrap-tvcj18.pdf | nn | | 13 | | ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans | TensorFlow | https://github.com/angeladai/ScanComplete | Apache-2.0 | | 14 | | AtLoc: Attention Guided Camera Localization | PyTorch | https://github.com/BingCS/AtLoc https://arxiv.org/abs/1909.03557 | BY-NC-SA 4.0 | | 15 | | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation | TensorFlow, cuDNN | https://github.com/charlesq34/pointnet | MIT License | | 16 | | PyTorch Implementation of DeepVO | PyTorch, CNN | https://github.com/ChiWeiHsiao/DeepVO-pytorch | nn | | 17 | | Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence. | PyTorch | https://github.com/chrischoy/FCGF | MIT License | | 18 | | Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020) | PyTorch | https://github.com/Colin97/MSN-Point-Cloud-Completion | Apache-2.0 | | 19 | | Real-Time Self-Adaptive Deep Stereo | TensorFlow | https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stereo | Apache-2.0 | | 20 | | Geometry meets semantics for semi-supervised monocular depth estimation - ACCV 2018 | TensorFlow | https://github.com/CVLAB-Unibo/Semantic-Mono-Depth | MIT License | | 21 | | BlenderProc: A procedural blender pipeline to generate images for deep learning | Blender | https://github.com/DLR-RM/BlenderProc | GPL-3.0 | | 22 | | SingleViewReconstruction: 3D Scene Reconstruction from a Single Viewport | TensorFlow | https://github.com/DLR-RM/SingleViewReconstruction | MIT License | | 23 | | NNCAP — Neural Network Complex Approach to Photogrammetry | | https://github.com/Dok11/nn-dldm | nn | | 24 | | Pytorch Implementation of Deeper Depth Prediction with Fully Convolutional Residual Networks | PyTorch | https://github.com/dontLoveBugs/FCRN_pytorch | nn | | 25 | | Improved Adversarial Systems for 3D Object Generation and Reconstruction | GAN | https://github.com/EdwardSmith1884/3D-IWGAN | MIT License | | 26 | | Deep Learning for Visual-Inertial Odometry | PyTorch, CNN | https://github.com/ElliotHYLee/Deep_Visual_Inertial_Odometry | MIT License | | 27 | | Machine Vision | List | https://github.com/Ewenwan/MVision | nn | | 28 | | Mesh R-CNN, an academic publication, presented at ICCV 2019 | PyTorch, R-CNN | https://github.com/facebookresearch/meshrcnn | BSD-3-Clause License | | 29 | | PyTorch3d is FAIR's library of reusable components for deep learning with 3D data. | PyTorch | https://github.com/facebookresearch/pytorch3d | BSD-3-Clause License | | 30 | | Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera | PyTorch | https://github.com/fangchangma/self-supervised-depth-completion | MIT License | | 31 | | Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image | PyTorch | https://github.com/fangchangma/sparse-to-dense | BSD License | | 32 | | Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image | PyTorch | https://github.com/fangchangma/sparse-to-dense.pytorch | nn | | 33 | | PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation | PyTorch | https://github.com/FangGet/PackNet-SFM-PyTorch | GPL-3.0 | | 34 | | InvSFM: Revealing Scenes by Inverting Structure from Motion Reconstructions [CVPR 2019] | TensorFlow | https://github.com/francescopittaluga/invsfm | MIT License | | 35 | | Deep Monocular Visual Odometry using PyTorch (Experimental) | PyTorch | https://github.com/fshamshirdar/DeepVO | nn | | 36 | | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation | PyTorch | https://github.com/fxia22/pointnet.pytorch | MIT License | | 37 | | Pix2Depth - Depth Map Estimation from Monocular Image | Keras | https://github.com/gautam678/Pix2Depth | GPL-3.0 | | 38 | | 3DRegNet: A Deep Neural Network for 3D Point Registration | TensorFlow | https://github.com/goncalo120/3DRegNet | MIT License | | 39 | | Neural 3D Mesh Renderer – Single-Image 3D Reconstruction using Neural Renderer | | https://github.com/hiroharu-kato/mesh_reconstruction | MIT License | | 40 | | Real-time Scalable Dense Surfel Mapping | | https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping | nn | | 41 | | MVDepthNet: real-time multiview depth estimation neural network | PyTorch | https://github.com/HKUST-Aerial-Robotics/MVDepthNet | nn | | 42 | | DeepMatchVO: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation | | https://github.com/hlzz/DeepMatchVO | MIT License | | 43 | | MIRorR: Matchable Image Retrieval by Learning from Surface Reconstruction | TensorFlow, CNN | https://github.com/hlzz/mirror | MIT License | | 44 | | Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction | Caffe | https://github.com/Huangying-Zhan/Depth-VO-Feat | non-commercial | | 45 | | Deep Learning 3D vision papers | papers, list, CN | https://github.com/huayong/dl-vision-papers | nn | | 46 | | Open3D PointNet implementation with PyTorch | PyTorch, jupyter, Open3D | https://github.com/intel-isl/Open3D-PointNet | MIT License | | 47 | | Semantic-TSDF for Self-driving Static Scene Reconstruction | PyTorch | https://github.com/irsisyphus/semantic-tsdf | MIT License | | 48 | | Weakly supervised 3D Reconstruction with Adversarial Constraint | | https://github.com/jgwak/McRecon | MIT License | | 49 | | Using Deep learning Technique for Stereo vision and 3D reconstruction | TensorFlow, CN | https://github.com/jiafeng5513/EvisionNet | nn | | 50 | | Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video | PyTorch | https://github.com/JiawangBian/SC-SfMLearner-Release | GPL-3.0 | | 51 | | Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries (official implementation) | PyTorch | https://github.com/JunjH/Revisiting_Single_Depth_Estimation | nn | | 52 | | Visualization of Convolutional Neural Networks for Monocular Depth Estimation (official implementation) | CNN, PyTorch | https://github.com/JunjH/Visualizing-CNNs-for-monocular-depth-estimation | MIT License | | 53 | | DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks | PyTorch | https://github.com/krrish94/DeepVO | nn | | 54 | | DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction | Tensorflow | https://github.com/laughtervv/DISN | nn | | 55 | | DeepTAM: Deep Tracking and Mapping | | https://github.com/lmb-freiburg/deeptam | GPL-3.0 | | 56 | | DeMoN: Depth and Motion Network | Tensorflow | https://github.com/lmb-freiburg/demon | GPL-3.0 | | 57 | | PyTorch implementation of CloudWalk's recent work DenseBody | PyTorch | https://github.com/Lotayou/densebody_pytorch | GPL-3.0 | | 58 | | Self-supervised learning for dense depth estimation in monocular endoscopy | Tensorflow, Torch | https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch | non-commercial | | 59 | | ContextDesc: Local Descriptor Augmentation with Cross-Modality Context | Tensorflow | https://github.com/lzx551402/contextdesc | nn | | 60 | | GL3D (Geometric Learning with 3D Reconstruction): a large-scale database created for 3D reconstruction and geometry-related learning problems | | https://github.com/lzx551402/GL3D | MIT License | | 61 | | Deeper Depth Prediction with Fully Convolutional Residual Networks (official implementation) | Tensorflow | https://github.com/MahmoudSelmy/DeeperDepthEstimation | nn | | 62 | | Fine-Tuning Vgg16 For Depth Estimation | Tensorflow | https://github.com/MahmoudSelmy/DepthEstimationVGG | nn | | 63 | | 3D reconstruction with neural networks using Tensorflow. See link for Video | | https://github.com/micmelesse/3D-reconstruction-with-Neural-Networks | nn | | 64 | | Learning Depth from Monocular Videos using Direct Methods | PyTorch | https://github.com/MightyChaos/LKVOLearner | BSD-3-Clause | | 65 | | PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition | Tensorflow | https://github.com/mikacuy/pointnetvlad | MIT License | | 66 | | Attempting to estimate topography of a region from image data | | https://github.com/nbelakovski/topography_neural_net | nn | | 67 | | DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs | Tensorflow | https://github.com/neycyanshi/DDRNet | MIT License | | 68 | | Monocular depth estimation from a single image | PyTorch | https://github.com/nianticlabs/monodepth2 | Copyright © Niantic, Inc. 2018. Patent Pending - non-commercial use only | | 69 | | 3D-RelNet: Joint Object and Relation Network for 3D prediction | Torch, jupyter | https://github.com/nileshkulkarni/relative3d | nn | | 70 | | PlaneRCNN detects and reconstructs piece-wise planar surfaces from a single RGB image | Torch, RCNN | https://github.com/NVlabs/planercnn | Copyright (c) 2018 NVIDIA Corp. All Rights Reserved. This work is licensed under the [Creative Commons Attribution NonCommercial ShareAlike 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). | | 71 | | OctoMap - An Efficient Probabilistic 3D Mapping Framework Based on Octrees. | | https://github.com/OctoMap/octomap | University of Freiburg, Copyright (C) 2009-2014, octomap: New BSD License, octovis and related libraries: GPL | | 72 | | Unsupervised Monocular Depth Estimation neural network MonoDepth in PyTorch (Unofficial implementation) | PyTorch | https://github.com/OniroAI/MonoDepth-PyTorch | nn | | 73 | | Learning to Sample: A learned sampling approach for point clouds | | https://github.com/orendv/learning_to_sample | MIT License | | 74 | | DeepMVS: Learning Multi-View Stereopsis | CNN, PyTorch | https://github.com/phuang17/DeepMVS | BSD 2-clause | | 75 | | DeepV2D: Video to Depth with Differentiable Structure from Motion | Tensorflow | https://github.com/princeton-vl/DeepV2D | nn | | 76 | | High Quality Monocular Depth Estimation via Transfer Learning | Tensorflow | https://github.com/priya-dwivedi/Deep-Learning/tree/master/depth_estimation | nn (GPL-3.0 ?) | | 77 | | Deep Single-View 3D Object Reconstruction with Visual Hull Embedding | CNN, Tensorflow | https://github.com/qweas120/PSVH-3d-reconstruction | MIT License | | 78 | | ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses,... | | https://github.com/ScanNet/ScanNet | Can be used with the restriction to give credit and include original Copyright | | 79 | | Visual inspection of bridges is customarily used to identify and evaluate faults | CNN | https://github.com/Shaggyshak/CS543_project_Image-based-Localization-of-Bridge-Defects-with-AR-Visualization | nn | | 80 | | Semantic 3D Occupancy Mapping through Efficient High Order CRFs | CNN | https://github.com/shichaoy/semantic_3d_mapping | BSD-3-Clause | | 81 | | Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene | | https://github.com/shubhtuls/factored3d | nn | | 82 | | Motion R-CNN codebase (old) | RCNN | https://github.com/simonmeister/old-motion-rcnn | MIT License | | 83 | | Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation | PyTorch | https://github.com/sshan-zhao/GASDA | nn | | 84 | | 3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera | | https://github.com/StanfordVL/3DSceneGraph | MIT License | | 85 | | Minkowski Engine is an auto-diff convolutional neural network library for high-dimensional sparse tensors | PyTorch | https://github.com/stanfordvl/MinkowskiEngine | MIT License | | 86 | | Learning Single-View 3D Reconstruction with Limited Pose Supervision (Official implementation) | Tensorflow | https://github.com/stevenygd/3d-recon | MIT License | | 87 | | VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera (Tensorflow version) | Tensorflow | https://github.com/timctho/VNect-tensorflow | Apache-2.0 | | 88 | | 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image | | https://github.com/val-iisc/3d-lmnet | MIT License | | 89 | | Learning to Find Good Correspondences | | https://github.com/vcg-uvic/learned-correspondence-release | For reserch and evaluation only. Commercial usage requires written approval | | 90 | | A Framework for the Volumetric Integration of Depth Images | | https://github.com/victorprad/InfiniTAM | non-commercial | | 91 | | Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation | Tensorflow | https://github.com/walsvid/Pixel2MeshPlusPlus | BSD-3-Clause | | 92 | | Adversarial Semantic Scene Completion from a Single Depth Image (Official implementation) | Tensorflow | https://github.com/wangyida/gan-depth-semantic3d | nn | | 93 | | SurfelWarp: Efficient Non-Volumetric Dynamic Reconstruction | | https://github.com/weigao95/surfelwarp | BSD-3-Clause | | 94 | | PCN: Point Completion Network | Tensorflow | https://github.com/wentaoyuan/pcn | MIT License | | 95 | | DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction | | https://github.com/Xharlie/DISN | nn | | 96 | | Real-time motion from structure | CNN | https://github.com/yan99033/CNN-SVO | nn | | 97 | | Dense 3D Object Reconstruction from a Single Depth View | Tensorflow | https://github.com/Yang7879/3D-RecGAN-extended | MIT License | | 98 | | Semi-supervised monocular depth map prediction | Tensorflow | https://github.com/Yevkuzn/semodepth | GPL-3.0 | | 99 | | 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration | Tensorflow | https://github.com/yewzijian/3DFeatNet | MIT License | | 100 | | Estimated Depth Map Helps Image Classification: Depth estimation with neural network, and learning on RGBD images | | https://github.com/yihui-he/Estimated-Depth-Map-Helps-Image-Classification | MIT License | | 101 | | Fit 3DMM to front and side face images simultaneously. | | https://github.com/Yinghao-Li/3DMM-fitting | nn | | 102 | | The Perfect Match: 3D Point Cloud Matching with Smoothed Densities | CNN, Tensorflow | https://github.com/zgojcic/3DSmoothNet | BSD-3-Clause | | 103 | | NeurVPS: Neural Vanishing Point Scanning via Conic Convolution | Tenosorflow | https://github.com/zhou13/neurvps | MIT License | | 104 | | LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image (Torch implementation) | Torch | https://github.com/zouchuhang/LayoutNet | MIT License | | 105 | | NeRF: Neural Radiance Fields | | https://github.com/bmild/nerf | MIT License | 10 | 106 | | Local Light Field Fusion at SIGGRAPH 2019 | | https://github.com/fyusion/llff | GPL-3.0 | 10 | 107 | | neural-volumes-learning-dynamic-renderable-volumes-from-images | | https://research.fb.com/publications/neural-volumes-learning-dynamic-renderable-volumes-from-images/
https://github.com/facebookresearch/neuralvolumes | BY-NC 4.0 | | 108 | | Learning Less is More - 6D Camera Localization via 3D Surface Regression | | https://github.com/vislearn/LessMore | BSD-3-Clause | | 109 | | Local features | | https://github.com/vcg-uvic/lf-net-release | | | 110 | | Pix2Vox | | https://github.com/hzxie/Pix2Vox | | | 111 | | PlanarReconstruction: Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding | pytorch | https://github.com/svip-lab/PlanarReconstruction | | | 112 | | Depth estimation with deep Neural networks | | https://medium.com/@omarbarakat1995/depth-estimation-with-deep-neural-networks-part-1-5fa6d2237d0d
https://medium.com/datadriveninvestor/depth-estimation-with-deep-neural-networks-part-2-81ee374888eb
https://github.com/MahmoudSelmy/DeeperDepthEstimation
https://github.com/MahmoudSelmy/DepthEstimationVGG/blob/master/README.md | | | 113 | | High Quality Monocular Depth Estimation via Transfer Learning | | https://github.com/ialhashim/DenseDepth
https://arxiv.org/abs/1812.11941 | | | 114 | | D3Feat | | https://github.com/XuyangBai/D3Feat | | | 115 | | Hierarchical Deep Stereo Matching on High Resolution Images | pytorch | https://github.com/gengshan-y/high-res-stereo | MIT | | 116 | | Structure-Aware Residual Pyramid Network for Monocular Depth Estimation | pytorch | https://github.com/Xt-Chen/SARPN | nn | | 117 | | Pytorch code to construct a 3D point cloud model from single RGB image. | pytorch | https://github.com/lkhphuc/pytorch-3d-point-cloud-generation | nn | | 118 | | Depth estimation from RGB images using fully convolutional neural networks | pytorch | https://github.com/karoly-hars/DE_resnet_unet_hyb | BSD-3-Clause | | 119 | | Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding | torch, tensorflow | https://github.com/svip-lab/PlanarReconstruction | MIT | | 120 | | TriDepth: Triangular Patch-based Deep Depth Prediction | PyTorch | https://github.com/syinari0123/tridepth | MIT | | 121 | | Depth Map Prediction from a Single Image using a Multi-Scale Deep Network | torch | https://github.com/imran3180/depth-map-prediction | nn | | 122 | | Hybrid CNN for Single Image Depth Estimation | torch | https://github.com/karoly-hars/DE_resnet_unet_hyb | BSD-3-Clause | | 123 | | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | torch | https://github.com/jiajunwu/marrnet | nn | | 124 | | Consistent Video Depth Estimation | | https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/ | nn | | 125 | | HF-Net: Robust Hierarchical Localization at Large Scale | torch, tensorflow | https://github.com/ethz-asl/hfnet | MIT | | 126 | | Hierarchical Deep Stereo Matching on High Resolution Images | pytorch | https://github.com/gengshan-y/high-res-stereo | MIT | | 127 | | Structure-Aware Residual Pyramid Network for Monocular Depth Estimation | pytorch | https://github.com/Xt-Chen/SARPN | nn | | 128 | | Pytorch code to construct a 3D point cloud model from single RGB image. | pytorch | https://github.com/lkhphuc/pytorch-3d-point-cloud-generation | nn | | 129 | | Depth estimation from RGB images using fully convolutional neural networks | pytorch | https://github.com/karoly-hars/DE_resnet_unet_hyb | BSD-3-Clause | | 130 | | Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding | torch, tensorflow | https://github.com/svip-lab/PlanarReconstruction | MIT | | 131 | | TriDepth: Triangular Patch-based Deep Depth Prediction | PyTorch | https://github.com/syinari0123/tridepth | MIT | | 132 | | Depth Map Prediction from a Single Image using a Multi-Scale Deep Network | torch | https://github.com/imran3180/depth-map-prediction | nn | | 133 | | Hybrid CNN for Single Image Depth Estimation | torch | https://github.com/karoly-hars/DE_resnet_unet_hyb | BSD-3-Clause | | 134 | | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | torch | https://github.com/jiajunwu/marrnet | nn | | 135 | | Consistent Video Depth Estimation | | https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/ | nn | | 136 | | HF-Net: Robust Hierarchical Localization at Large Scale | torch, tensorflow | https://github.com/ethz-asl/hfnet | MIT | | 137 | 138 | 139 | 140 | **Other Projects** 141 | 142 | | TITLE | KEYWORDS | URL | LICENSE | 143 | | ------------------------------------------------------------ | --------------------- | ------------------------------------------------------------ | ------- | 144 | | 3D-Scene-GAN: Three-dimensional Scene Reconstruction with Generative Adversarial Networks | paper | https://openreview.net/forum?id=SkNEsmJwf | | 145 | | Google: Deep Learning Depth Prediction | magazine article, GER | https://www.digitalproduction.com/2019/05/27/google-deep-learning-depth-prediction/ | | 146 | | SLAM and Deep Leraning for 3D Indoor Scene Understanding | PhD thesis | https://www.doc.ic.ac.uk/~ajd/Publications/McCormac-J-2019-PhD-Thesis.pdf | | 147 | | Dense 3D Object Reconstruction from a Single Depth View | 3D-RecGAN++ | https://arxiv.org/abs/1802.00411 | | 148 | | Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction | | https://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html | | 149 | | Depth Estimation from a Single RGB Image | | http://campar.in.tum.de/Chair/ProjectDepthPrediction | | 150 | | Deep Fundamental Matrix Estimation | | http://vladlen.info/papers/deep-fundamental.pdf | | 151 | | depth_estimation | | https://towardsdatascience.com/depth-estimation-on-camera-images-using-densenets-ac454caa893 | | 152 | | **3D-Machine-Learning List** | | https://github.com/timzhang642/3D-Machine-Learning | | 153 | | **DEEP LEARNING-BASED 3D OBJECT RECONSTRUCTION - A SURVEY - Image-based 3D Object Reconstruction:State-of-the-Art and Trends in the DeepLearning Era** | | https://arxiv.org/pdf/1906.06543.pdf | | 154 | | | | | | 155 | | | | | | 156 | | | | | | 157 | | | | | | 158 | 159 | I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs (CVPR 2023) https://github.com/jingsenzhu/i2-sdf MIT 160 | 161 | https://github.com/lioryariv/idr 162 | 163 | https://github.com/autonomousvision/differentiable_volumetric_rendering 164 | 165 | https://github.com/Dok11/surface-match-dataset 166 | 167 | Image-based 3D Object Reconstruction:State-of-the-Art and Trends in the DeepLearning Era https://arxiv.org/pdf/1906.06543v3.pdf 168 | 169 | Dense 3D Object Reconstructionfrom a Single Depth View https://arxiv.org/pdf/1802.00411v2.pdf 170 | 171 | https://dagshub.com/OperationSavta/SavtaDepth https://colab.research.google.com/drive/1XU4DgQ217_hUMU1dllppeQNw3pTRlHy1?usp=sharing https://huggingface.co/spaces/kingabzpro/savtadepth MIT License 172 | 173 | https://github.com/gradslam/gradslam pyTorch 174 | 175 | https://github.com/ventusff/neurecon 176 | 177 | https://github.com/theICTlab/3DUNDERWORLD-SLS-GPU_CPU 178 | --------------------------------------------------------------------------------