# PHI-MVS: Plane Hypothesis Inference Multi-view Stereo for Large-Scale Scene Reconstruction

arXiv: https://arxiv.org/abs/2104.06165 · HTML: https://mlsanity.com/paper/2104.06165/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [Multi-Scale Geometric Consistency Guided Multi-View Stereo](https://mlsanity.com/paper/1904.08103/) (2019)
- [Planar Prior Assisted PatchMatch Multi-View Stereo](https://mlsanity.com/paper/1912.11744/) (2019)
- [Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation](https://mlsanity.com/paper/1912.03001/) (2019)
- [PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network](https://mlsanity.com/paper/2007.07714/) (2020)
- [TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo](https://mlsanity.com/paper/1903.10929/) (2019)

## Built on by (most cited first, 8 of 8)

- [Neural 3D Scene Reconstruction with the Manhattan-world Assumption](https://mlsanity.com/paper/2205.02836/) (2022)
- [DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo](https://mlsanity.com/paper/2412.11578/) (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)
- [PlaNeRF: SVD Unsupervised 3D Plane Regularization for NeRF Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2305.16914/) (2023)
- [NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction](https://mlsanity.com/paper/2210.06853/) (2022)
- [DVP-MVS++: Synergize Depth-Normal-Edge and Harmonized Visibility Prior for Multi-View Stereo](https://mlsanity.com/paper/2506.13215/) (2025)
- [P2SDF for Neural Indoor Scene Reconstruction](https://mlsanity.com/paper/2303.00236/) (2023)
- [NeRO: Neural Road Surface Reconstruction](https://mlsanity.com/paper/2405.10554/) (2024)

## Abstract (arXiv)

PatchMatch based Multi-view Stereo (MVS) algorithms have achieved great success in large-scale scene reconstruction tasks. However, reconstruction of texture-less planes often fails as similarity measurement methods may become ineffective on these regions. Thus, a new plane hypothesis inference strategy is proposed to handle the above issue. The procedure consists of two steps: First, multiple plane hypotheses are generated using filtered initial depth maps on regions that are not successfully recovered; Second, depth hypotheses are selected using Markov Random Field (MRF). The strategy can significantly improve the completeness of reconstruction results with only acceptable computing time increasing. Besides, a new acceleration scheme similar to dilated convolution can speed up the depth map estimating process with only a slight influence on the reconstruction. We integrated the above ideas into a new MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). The result of PHI-MVS is validated on ETH3D public benchmarks, and it demonstrates competing performance against the state-of-the-art.