# Planar Prior Assisted PatchMatch Multi-View Stereo

arXiv: https://arxiv.org/abs/1912.11744 · HTML: https://mlsanity.com/paper/1912.11744/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 30 of 57)

- [Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction](https://mlsanity.com/paper/2205.15848/) (2022)
- [NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo](https://mlsanity.com/paper/2109.01129/) (2021)
- [Neural 3D Scene Reconstruction with the Manhattan-world Assumption](https://mlsanity.com/paper/2205.02836/) (2022)
- [Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation](https://mlsanity.com/paper/2201.01501/) (2022)
- [IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.05126/) (2021)
- [Multiview Stereo with Cascaded Epipolar RAFT](https://mlsanity.com/paper/2205.04502/) (2022)
- [PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2203.12082/) (2022)
- [HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization](https://mlsanity.com/paper/2302.14340/) (2023)
- [SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization](https://mlsanity.com/paper/2401.06385/) (2024)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [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)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo](https://mlsanity.com/paper/2201.07609/) (2022)
- [GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering](https://mlsanity.com/paper/2411.07478/) (2024)
- [SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint](https://mlsanity.com/paper/2503.13721/) (2025)
- [Neural Vector Fields for Implicit Surface Representation and Inference](https://mlsanity.com/paper/2204.06552/) (2022)
- [Para-Lane: Multi-Lane Dataset Registering Parallel Scans for Benchmarking Novel View Synthesis](https://mlsanity.com/paper/2502.15635/) (2025)
- [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)
- [PlaNeRF: SVD Unsupervised 3D Plane Regularization for NeRF Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2305.16914/) (2023)
- [SmartPortraits: Depth Powered Handheld Smartphone Dataset of Human Portraits for State Estimation, Reconstruction and Synthesis](https://mlsanity.com/paper/2204.10211/) (2022)
- [Hierarchical Prior Mining for Non-local Multi-View Stereo](https://mlsanity.com/paper/2303.09758/) (2023)
- [TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering](https://mlsanity.com/paper/2303.15060/) (2023)
- [Non-local Recurrent Regularization Networks for Multi-view Stereo](https://mlsanity.com/paper/2110.06436/) (2021)
- [Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets](https://mlsanity.com/paper/2310.00530/) (2023)
- [ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency](https://mlsanity.com/paper/2412.01402/) (2024)
- [NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction](https://mlsanity.com/paper/2210.06853/) (2022)
- [DeLiRa: Self-Supervised Depth, Light, and Radiance Fields](https://mlsanity.com/paper/2304.02797/) (2023)
- [Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization](https://mlsanity.com/paper/2412.20328/) (2024)

## Abstract (arXiv)

The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured areas usually exhibit strong planarity, planar models are advantageous to the depth estimation of low-textured areas. On the other hand, PatchMatch multi-view stereo is very efficient for its sampling and propagation scheme. By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost. This novel cost takes both photometric consistency and planar compatibility into consideration, making it suited for the depth estimation of both non-planar and planar regions. Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance.