# RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

arXiv: https://arxiv.org/abs/2203.03949 · HTML: https://mlsanity.com/paper/2203.03949/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 38 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 30 of 31)

- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [PatchmatchNet: Learned Multi-View Patchmatch Stereo](https://mlsanity.com/paper/2012.01411/) (2020)
- [NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo](https://mlsanity.com/paper/2109.01129/) (2021)
- [DeRF: Decomposed Radiance Fields](https://mlsanity.com/paper/2011.12490/) (2020)
- [Cost Volume Pyramid Based Depth Inference for Multi-View Stereo](https://mlsanity.com/paper/1912.08329/) (2019)
- [Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness](https://mlsanity.com/paper/1911.12012/) (2019)
- [SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis](https://mlsanity.com/paper/1708.01749/) (2017)
- [Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images](https://mlsanity.com/paper/2007.09892/) (2020)
- [Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking](https://mlsanity.com/paper/2007.10872/) (2020)
- [AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network](https://mlsanity.com/paper/2108.03824/) (2021)
- [Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume](https://mlsanity.com/paper/1912.11746/) (2019)
- [Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency](https://mlsanity.com/paper/1905.02706/) (2019)
- [PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network](https://mlsanity.com/paper/2007.07714/) (2020)
- [Generalized Binary Search Network for Highly-Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.02338/) (2021)
- [Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation](https://mlsanity.com/paper/2104.05374/) (2021)
- [Digging into Uncertainty in Self-supervised Multi-view Stereo](https://mlsanity.com/paper/2108.12966/) (2021)
- [MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry](https://mlsanity.com/paper/1908.11526/) (2019)
- [M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network](https://mlsanity.com/paper/2005.00363/) (2020)
- [Self-supervised Learning of Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2104.02972/) (2021)

## Built on by (most cited first, 30 of 30)

- [MVImgNet: A Large-scale Dataset of Multi-view Images](https://mlsanity.com/paper/2303.06042/) (2023)
- [GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction](https://mlsanity.com/paper/2309.02436/) (2023)
- [SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates](https://mlsanity.com/paper/2303.13582/) (2023)
- [ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction](https://mlsanity.com/paper/2305.18832/) (2023)
- [Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views](https://mlsanity.com/paper/2504.20378/) (2025)
- [GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow](https://mlsanity.com/paper/2502.17288/) (2025)
- [UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets](https://mlsanity.com/paper/2403.05086/) (2024)
- [OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow](https://mlsanity.com/paper/2402.12792/) (2024)
- [C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction](https://mlsanity.com/paper/2306.10003/) (2023)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding](https://mlsanity.com/paper/2403.03608/) (2024)
- [Q-SLAM: Quadric Representations for Monocular SLAM](https://mlsanity.com/paper/2403.08125/) (2024)
- [nLMVS-Net: Deep Non-Lambertian Multi-View Stereo](https://mlsanity.com/paper/2207.11876/) (2022)
- [Range-Agnostic Multi-View Depth Estimation With Keyframe Selection](https://mlsanity.com/paper/2401.14401/) (2024)
- [Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction](https://mlsanity.com/paper/2409.03634/) (2024)
- [PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Grouping](https://mlsanity.com/paper/2501.01677/) (2025)
- [CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning](https://mlsanity.com/paper/2503.08219/) (2025)
- [Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity](https://mlsanity.com/paper/2211.16905/) (2022)
- [ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field](https://mlsanity.com/paper/2401.08140/) (2024)
- [Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization](https://mlsanity.com/paper/2303.17088/) (2023)
- [LucidFusion: Reconstructing 3D Gaussians with Arbitrary Unposed Images](https://mlsanity.com/paper/2410.15636/) (2024)
- [Radiant Triangle Soup with Soft Connectivity Forces for 3D Reconstruction and Novel View Synthesis](https://mlsanity.com/paper/2505.23642/) (2025)
- [SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation](https://mlsanity.com/paper/2604.10218/) (2026)
- [ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo](https://mlsanity.com/paper/2308.02191/) (2023)
- [Sparse2DGS: Sparse-View Surface Reconstruction using 2D Gaussian Splatting with Dense Point Cloud](https://mlsanity.com/paper/2505.19854/) (2025)
- [Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation](https://mlsanity.com/paper/2303.06418/) (2023)
- [Prism: Semi-Supervised Multi-View Stereo with Monocular Structure Priors](https://mlsanity.com/paper/2412.05771/) (2024)
- [Boosting Multi-View Stereo with Depth Foundation Model in the Absence of Real-World Labels](https://mlsanity.com/paper/2504.11845/) (2025)
- [Multi-view dense image matching with similarity learning and geometry priors](https://mlsanity.com/paper/2505.11264/) (2025)
- [3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis](https://mlsanity.com/paper/2604.11211/) (2026)

## Abstract (arXiv)

Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in real scenarios observe non-Lambertian surfaces and experience occlusions. In this work, we propose a novel approach with neural rendering (RC-MVSNet) to solve such ambiguity issues of correspondences among views. Specifically, we impose a depth rendering consistency loss to constrain the geometry features close to the object surface to alleviate occlusions. Concurrently, we introduce a reference view synthesis loss to generate consistent supervision, even for non-Lambertian surfaces. Extensive experiments on DTU and Tanks&Temples benchmarks demonstrate that our RC-MVSNet approach achieves state-of-the-art performance over unsupervised MVS frameworks and competitive performance to many supervised methods.The code is released at https://github.com/Boese0601/RC-MVSNet