- Volumetric methods (SurfaceNet)
- Depthmap based methods (MVSNet/R-MVSNet and so on)
( 💻 means code available)
- 💻 SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis [paper] [Github] [T-PAMI]
- Learning a Multi-View Stereo Machine [paper] (LSMs can produce two kinds of outputs - voxel occupancy grids decoded from 3D Grid or per-view depth maps decoded after a projection operation.)
- Learned Multi-Patch Similarity [paper] [supp] (Note: Learning to measure multi-image patch similiarity, NOT end-to-end learning MVS pipeline)
- 💻 Point-Based Multi-View Stereo Network [paper] [supp] [Github] [T-PAMI] (Point-MVSNet performs multi-view stereo reconstruction in a coarse-to-fine fashion, learning to predict the 3D flow of each point to the groundtruth surface based on geometry priors and 2D image appearance cues)
- P-MVSNet: Learning Patch-wise Matching Confidence Aggregation for Multi-view Stereo [paper]
- MVSCRF: Learning Multi-view Stereo with Conditional Random Fields [paper]
- Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume [paper] [Github]
-
💻 Cascade Cost Volume for High-Resolutoin Multi-View Stereo and Stereo Matching [paper] [Github]
-
💻 Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness [paper] [supp] [Github]
-
💻 Cost Volume Pyramid Based Depth Inference for Multi-View Stereo [paper] [supp] [Github]
-
💻 Fast-MVSNet: Sparse-to-Dense Multi-View Stereo with Learned Propagation and Gauss-Newton Refinement [paper] [supp] [Github]
-
Attention-Aware Multi-View Stereo [paper]
-
💻 A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset [paper] [Github] [data]
- 💻 Pyramid Multi-view Stereo Net with Self-adaptive View aggregation [paper] [Github]
- 💻 Dense Hybird Recurrent Multi-view Stereo Net with Dynamic Consistency Checking [paper] [Github]
- Long-range Attention Network for Multi-View Stereo [paper]
- 💻 AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network [paper] [supp] [Github]
- EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo [paper]
- Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo [paper] [supp]
-
💻 IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo [paper] [supp] [Github]
-
💻 Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation and Focal Loss [paper][supp] [Github]
-
💻 RayMVSNet: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo [paper] [supp] [Github]
-
Non-Parametric Depth Distribution Modelling Based Depth Inference for Multi-View Stereo [paper] [supp]
-
💻 TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers [paper] [supp] [Github]
-
💻 Generalized Binary Search Network for Highly-Efficient Multi-View Stereo [paper] [supp] [Github]
-
💻 Efficient Multi-View Stereo by Iterative Dynamic Cost Volume [paper] [supp] [Github]
-
💻 MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions [paper] [supp] [Github]
- 💻 MVSTER: Epipolar Transformer for Efficient Multi-View Stereo [paper] [Github]
- 💻 Multiview Stereo with Cascaded Epipolar RAFT [paper] [Github]
- MVSNet++: Learning Depth-Based Attention Pyramid Features for Multi-View Stereo. IEEE Transactions on Image Processing [paper]
- HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction From High-Resolution Images. IEEE Access [paper]
- 💻 AACVP-MVSNet: Attention-aware cost volume pyramid based multi-view stereo network for 3D reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing [paper] [Github]
- Learning Inverse Depth Regression for Pixelwise Visibility-Aware Multi-View Stereo Networks. International Journal of Computer Vision [paper]
- Sparse prior guided deep multi-view stereo. Computers & Graphics [paper]
- A Survey on Deep Learning Techniques for Stereo-based Depth Estimation. IEEE T-PAMI [ArXiv] [IEEE Xplore]
- Deep Learning for Multi-view Stereo via Plane Sweep: A Survey [paper]
- Multi-view stereo in the Deep Learning Era: A comprehensive review [paper]
- 🎓 Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery, Johannes L. Schönberger@ETH Zürich
- 🎓 Learning Large-scale Multi-view Stereopsis, Yao Yao@HKUST
-
Middlebury [CVPR06']
-
EPFL [CVPR08']
- On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery [paper]
-
DTU [CVPR2014, IJCV2016]
-
Tanks and Temples [ACM ToG2017]
- Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction [paper] [supp] [website] [Github] [leaderboard]
-
ETH3D [CVPR2017]
-
BlendedMVS [CVPR2020]
-
GigaMVS [T-PAMI2021]
-
Multi-sensor large-scale dataset for multi-view 3D reconstruction [CVPR2023]
- Chinese Style Architectures
- http://vision.ia.ac.cn/zh/data/index.html, provided by CASIA.
- Western Style Architectures
- https://colmap.github.io/datasets.html, provided by COLMAP.
- ImageDataset_SceauxCastle, provided by OpenMVG.
- Aerial Dataset
- http://gpcv.whu.edu.cn/data/WHU_MVS_Stereo_dataset.html, provided by WHU.
- Awesome-MVS1
- Awesome-MVS2
- PatchMatch Multi-view Stereo
- Unsupervised Multi-view Stereo
- multi-view-3d-reconstruction
- ultra-large-scale 3D Reconstruction: GigaMVS
- Semantic multi-view 3D Reconstruction
如果想看经典MVS论文介绍，可以参照👉中文论文讲解