# Deep Multi-View Stereo gone wild

arXiv: https://arxiv.org/abs/2104.15119 · HTML: https://mlsanity.com/paper/2104.15119/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://mlsanity.com/paper/2003.10432/) (2020)
- [Cost Volume Pyramid Based Depth Inference for Multi-View Stereo](https://mlsanity.com/paper/1912.08329/) (2019)
- [Point-Based Multi-View Stereo Network](https://mlsanity.com/paper/1908.04422/) (2019)
- [SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis](https://mlsanity.com/paper/1708.01749/) (2017)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement](https://mlsanity.com/paper/2003.13017/) (2020)
- [Visibility-aware Multi-view Stereo Network](https://mlsanity.com/paper/2008.07928/) (2020)
- [Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking](https://mlsanity.com/paper/2007.10872/) (2020)
- [Computing the Stereo Matching Cost with a Convolutional Neural Network](https://mlsanity.com/paper/1409.4326/) (2014)
- [RANSAC-Flow: generic two-stage image alignment](https://mlsanity.com/paper/2004.01526/) (2020)
- [Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation](https://mlsanity.com/paper/1912.03001/) (2019)
- [Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency](https://mlsanity.com/paper/1905.02706/) (2019)
- [Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation](https://mlsanity.com/paper/2104.05374/) (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)

## Built on by (most cited first, 11 of 11)

- [Improving neural implicit surfaces geometry with patch warping](https://mlsanity.com/paper/2112.09648/) (2021)
- [TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers](https://mlsanity.com/paper/2111.14600/) (2021)
- [Neural 3D Reconstruction in the Wild](https://mlsanity.com/paper/2205.12955/) (2022)
- [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)
- [GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo](https://mlsanity.com/paper/2310.19583/) (2023)
- [NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis](https://mlsanity.com/paper/2108.03880/) (2021)
- [Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss](https://mlsanity.com/paper/2206.10360/) (2022)
- [PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction](https://mlsanity.com/paper/2203.02156/) (2022)
- [Radiance Field Learners As UAV First-Person Viewers](https://mlsanity.com/paper/2408.05533/) (2024)
- [Blending 3D Geometry and Machine Learning for Multi-View Stereopsis](https://mlsanity.com/paper/2505.03470/) (2025)

## Abstract (arXiv)

Deep multi-view stereo (MVS) methods have been developed and extensively compared on simple datasets, where they now outperform classical approaches. In this paper, we ask whether the conclusions reached in controlled scenarios are still valid when working with Internet photo collections. We propose a methodology for evaluation and explore the influence of three aspects of deep MVS methods: network architecture, training data, and supervision. We make several key observations, which we extensively validate quantitatively and qualitatively, both for depth prediction and complete 3D reconstructions. First, complex unsupervised approaches cannot train on data in the wild. Our new approach makes it possible with three key elements: upsampling the output, softmin based aggregation and a single reconstruction loss. Second, supervised deep depthmap-based MVS methods are state-of-the art for reconstruction of few internet images. Finally, our evaluation provides very different results than usual ones. This shows that evaluation in uncontrolled scenarios is important for new architectures.