# Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation

arXiv: https://arxiv.org/abs/1912.03001 · HTML: https://mlsanity.com/paper/1912.03001/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 30 of 44)

- [Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation](https://mlsanity.com/paper/2201.01501/) (2022)
- [AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network](https://mlsanity.com/paper/2108.03824/) (2021)
- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth](https://mlsanity.com/paper/2208.02541/) (2022)
- [Multiview Stereo with Cascaded Epipolar RAFT](https://mlsanity.com/paper/2205.04502/) (2022)
- [MVSTER: Epipolar Transformer for Efficient Multi-View Stereo](https://mlsanity.com/paper/2204.07346/) (2022)
- [Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2109.12484/) (2021)
- [TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo](https://mlsanity.com/paper/2111.07418/) (2021)
- [Self-supervised Learning of Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2104.02972/) (2021)
- [3DVNet: Multi-View Depth Prediction and Volumetric Refinement](https://mlsanity.com/paper/2112.00202/) (2021)
- [WT-MVSNet: Window-based Transformers for Multi-view Stereo](https://mlsanity.com/paper/2205.14319/) (2022)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [Curvature-guided dynamic scale networks for Multi-view Stereo](https://mlsanity.com/paper/2112.05999/) (2021)
- [Deep Learning for Multi-View Stereo via Plane Sweep: A Survey](https://mlsanity.com/paper/2106.15328/) (2021)
- [RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo](https://mlsanity.com/paper/2307.10233/) (2023)
- [Multi-View Stereo with Transformer](https://mlsanity.com/paper/2112.00336/) (2021)
- [Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function](https://mlsanity.com/paper/2201.01016/) (2022)
- [When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo](https://mlsanity.com/paper/2309.17218/) (2023)
- [GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement](https://mlsanity.com/paper/2406.05649/) (2024)
- [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)
- [CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo](https://mlsanity.com/paper/2305.10320/) (2023)
- [PHI-MVS: Plane Hypothesis Inference Multi-view Stereo for Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2104.06165/) (2021)
- [MVLayoutNet:3D layout reconstruction with multi-view panoramas](https://mlsanity.com/paper/2112.06133/) (2021)
- [GENA3D: Generative Amodal 3D Modeling by Bridging 2D Priors and 3D Coherence](https://mlsanity.com/paper/2511.21945/) (2025)
- [Semi-supervised Deep Multi-view Stereo](https://mlsanity.com/paper/2207.11699/) (2022)
- [Pixel-Aligned Multi-View Generation with Depth Guided Decoder](https://mlsanity.com/paper/2408.14016/) (2024)
- [Disjoint Pose and Shape for 3D Face Reconstruction](https://mlsanity.com/paper/2308.13903/) (2023)
- [Geometry-Informed Distance Candidate Selection for Adaptive Lightweight Omnidirectional Stereo Vision with Fisheye Images](https://mlsanity.com/paper/2405.05355/) (2024)
- [SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing](https://mlsanity.com/paper/2405.17140/) (2024)

## Abstract (arXiv)

n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction. Different from using mean square variance to generate cost volume in previous deep-learning based MVS methods, our VA-MVSNet incorporates the cost variances in different views with small extra memory consumption by introducing two novel self-adaptive view aggregations: pixel-wise view aggregation and voxel-wise view aggregation. To further boost the robustness and completeness of 3D point cloud reconstruction, we extend VA-MVSNet with pyramid multi-scale images input as PVA-MVSNet, where multi-metric constraints are leveraged to aggregate the reliable depth estimation at the coarser scale to fill in the mismatched regions at the finer scale. Experimental results show that our approach establishes a new state-of-the-art on the textslDTU dataset with significant improvements in the completeness and overall quality, and has strong generalization by achieving a comparable performance as the state-of-the-art methods on the textslTanks and Temples benchmark. Our codebase is at hyperlinkhttps://github.com/yhw-yhw/PVAMVSNethttps://github.com/yhw-yhw/PVAMVSNet