# MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry

arXiv: https://arxiv.org/abs/1908.11526 · HTML: https://mlsanity.com/paper/1908.11526/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

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

- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [Improving neural implicit surfaces geometry with patch warping](https://mlsanity.com/paper/2112.09648/) (2021)
- [FWD: Real-time Novel View Synthesis with Forward Warping and Depth](https://mlsanity.com/paper/2206.08355/) (2022)
- [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)
- [RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering](https://mlsanity.com/paper/2203.03949/) (2022)
- [Self-supervised Learning of Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2104.02972/) (2021)
- [LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives](https://mlsanity.com/paper/2404.09748/) (2024)
- [MVSAnywhere: Zero-Shot Multi-View Stereo](https://mlsanity.com/paper/2503.22430/) (2025)
- [KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo](https://mlsanity.com/paper/2207.10425/) (2022)
- [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)
- [Prior-Guided Multi-View 3D Head Reconstruction](https://mlsanity.com/paper/2107.04277/) (2021)
- [Deep Multi-View Stereo gone wild](https://mlsanity.com/paper/2104.15119/) (2021)
- [DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis](https://mlsanity.com/paper/2208.06674/) (2022)
- [DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications](https://mlsanity.com/paper/2406.10068/) (2024)
- [Semi-supervised Deep Multi-view Stereo](https://mlsanity.com/paper/2207.11699/) (2022)
- [CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning](https://mlsanity.com/paper/2503.08219/) (2025)
- [Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data](https://mlsanity.com/paper/2109.11573/) (2021)
- [PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction](https://mlsanity.com/paper/2203.02156/) (2022)
- [Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity](https://mlsanity.com/paper/2211.16905/) (2022)
- [SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing](https://mlsanity.com/paper/2405.17140/) (2024)
- [Improving Neural Surface Reconstruction with Feature Priors from Multi-View Image](https://mlsanity.com/paper/2408.02079/) (2024)
- [HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces](https://mlsanity.com/paper/2302.06793/) (2023)
- [ViM-Disparity: Bridging the Gap of Speed, Accuracy and Memory for Disparity Map Generation](https://mlsanity.com/paper/2412.16745/) (2024)
- [ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo](https://mlsanity.com/paper/2308.02191/) (2023)
- [Geometric Constraints in Deep Learning Frameworks: A Survey](https://mlsanity.com/paper/2403.12431/) (2024)
- [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)
- [Blending 3D Geometry and Machine Learning for Multi-View Stereopsis](https://mlsanity.com/paper/2505.03470/) (2025)

## Abstract (arXiv)

The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.