# PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network

arXiv: https://arxiv.org/abs/2007.07714 · HTML: https://mlsanity.com/paper/2007.07714/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 30 of 35)

- [Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction](https://mlsanity.com/paper/2205.15848/) (2022)
- [IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.05126/) (2021)
- [MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth](https://mlsanity.com/paper/2208.02541/) (2022)
- [MVSTER: Epipolar Transformer for Efficient Multi-View Stereo](https://mlsanity.com/paper/2204.07346/) (2022)
- [A Benchmark and a Baseline for Robust Multi-view Depth Estimation](https://mlsanity.com/paper/2209.06681/) (2022)
- [Generalized Binary Search Network for Highly-Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.02338/) (2021)
- [Uncertainty-Aware Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2202.13071/) (2022)
- [RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering](https://mlsanity.com/paper/2203.03949/) (2022)
- [PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2203.12082/) (2022)
- [Self-supervised Learning of Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2104.02972/) (2021)
- [Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2110.05594/) (2021)
- [HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization](https://mlsanity.com/paper/2302.14340/) (2023)
- [PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility](https://mlsanity.com/paper/2108.08943/) (2021)
- [Curvature-guided dynamic scale networks for Multi-view Stereo](https://mlsanity.com/paper/2112.05999/) (2021)
- [Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2205.03783/) (2022)
- [CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo](https://mlsanity.com/paper/2305.10320/) (2023)
- [TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo](https://mlsanity.com/paper/2308.09990/) (2023)
- [PHI-MVS: Plane Hypothesis Inference Multi-view Stereo for Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2104.06165/) (2021)
- [SC-NeuS: Consistent Neural Surface Reconstruction from Sparse and Noisy Views](https://mlsanity.com/paper/2307.05892/) (2023)
- [Semi-supervised Deep Multi-view Stereo](https://mlsanity.com/paper/2207.11699/) (2022)
- [IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions](https://mlsanity.com/paper/2111.14420/) (2021)
- [CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement](https://mlsanity.com/paper/2404.02225/) (2024)
- [Lightweight and Accurate Multi-View Stereo with Confidence-Aware Diffusion Model](https://mlsanity.com/paper/2509.15220/) (2025)
- [Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity](https://mlsanity.com/paper/2211.16905/) (2022)
- [DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo](https://mlsanity.com/paper/2212.06626/) (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)
- [A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding](https://mlsanity.com/paper/2411.01893/) (2024)
- [Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion](https://mlsanity.com/paper/2503.11088/) (2025)
- [DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range](https://mlsanity.com/paper/2103.14275/) (2021)
- [Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo](https://mlsanity.com/paper/2207.12032/) (2022)

## Abstract (arXiv)

Recently, learning-based multi-view stereo methods have achieved promising results. However, they all overlook the visibility difference among different views, which leads to an indiscriminate multi-view similarity definition and greatly limits their performance on datasets with strong viewpoint variations. In this paper, a Pixelwise Visibility-aware multi-view Stereo Network (PVSNet) is proposed for robust dense 3D reconstruction. We present a pixelwise visibility network to learn the visibility information for different neighboring images before computing the multi-view similarity, and then construct an adaptive weighted cost volume with the visibility information. Moreover, we present an anti-noise training strategy that introduces disturbing views during model training to make the pixelwise visibility network more distinguishable to unrelated views, which is different with the existing learning methods that only use two best neighboring views for training. To the best of our knowledge, PVSNet is the first deep learning framework that is able to capture the visibility information of different neighboring views. In this way, our method can be generalized well to different types of datasets, especially the ETH3D high-res benchmark with strong viewpoint variations. Extensive experiments show that PVSNet achieves the state-of-the-art performance on different datasets.