# Multi-View Stereo Network with attention thin volume

arXiv: https://arxiv.org/abs/2110.08556 · HTML: https://mlsanity.com/paper/2110.08556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019)
- [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)
- [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)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation](https://mlsanity.com/paper/1912.03001/) (2019)
- [Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction](https://mlsanity.com/paper/2011.12722/) (2020)

## Abstract (arXiv)

We propose an efficient multi-view stereo (MVS) network for infering depth value from multiple RGB images. Recent studies have shown that mapping the geometric relationship in real space to neural network is an essential topic of the MVS problem. Specifically, these methods focus on how to express the correspondence between different views by constructing a nice cost volume. In this paper, we propose a more complete cost volume construction approach based on absorbing previous experience. First of all, we introduce the self-attention mechanism to fully aggregate the dominant information from input images and accurately model the long-range dependency, so as to selectively aggregate reference features. Secondly, we introduce the group-wise correlation to feature aggregation, which greatly reduces the memory and calculation burden. Meanwhile, this method enhances the information interaction between different feature channels. With this approach, a more lightweight and efficient cost volume is constructed. Finally we follow the coarse to fine strategy and refine the depth sampling range scale by scale with the help of uncertainty estimation. We further combine the previous steps to get the attention thin volume. Quantitative and qualitative experiments are presented to demonstrate the performance of our model.