# GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo

arXiv: https://arxiv.org/abs/2404.07992 · HTML: https://mlsanity.com/paper/2404.07992/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth](https://mlsanity.com/paper/2208.02541/) (2022)
- [Boosting Multi-view Stereo with Late Cost Aggregation](https://mlsanity.com/paper/2401.11751/) (2024)

## Built on by (most cited first, 16 of 16)

- [G3R: Gradient Guided Generalizable Reconstruction](https://mlsanity.com/paper/2409.19405/) (2024)
- [Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views](https://mlsanity.com/paper/2504.20378/) (2025)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification](https://mlsanity.com/paper/2407.11840/) (2024)
- [ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency](https://mlsanity.com/paper/2412.01402/) (2024)
- [GENA3D: Generative Amodal 3D Modeling by Bridging 2D Priors and 3D Coherence](https://mlsanity.com/paper/2511.21945/) (2025)
- [MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network](https://mlsanity.com/paper/2507.11333/) (2025)
- [ICG-MVSNet: Learning Intra-view and Cross-view Relationships for Guidance in Multi-View Stereo](https://mlsanity.com/paper/2503.21525/) (2025)
- [MVSMamba: Multi-View Stereo with State Space Model](https://mlsanity.com/paper/2511.01315/) (2025)
- [Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues](https://mlsanity.com/paper/2506.05655/) (2025)
- [Boosting Multi-View Stereo with Depth Foundation Model in the Absence of Real-World Labels](https://mlsanity.com/paper/2504.11845/) (2025)
- [Blending 3D Geometry and Machine Learning for Multi-View Stereopsis](https://mlsanity.com/paper/2505.03470/) (2025)
- [IA-MVS: Instance-Focused Adaptive Depth Sampling for Multi-View Stereo](https://mlsanity.com/paper/2505.12714/) (2025)
- [GeoDiff: Geometry-Guided Diffusion for Metric Depth Estimation](https://mlsanity.com/paper/2510.18291/) (2025)
- [Reference-Free Omnidirectional Stereo Matching via Multi-View Consistency Maximization](https://mlsanity.com/paper/2603.15019/) (2026)
- [M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo](https://mlsanity.com/paper/2608.20788/) (2026)

## Abstract (arXiv)

Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space, both are unable to handle geometric inconsistency in the cost volume effectively. In this paper, we propose GoMVS to aggregate geometrically consistent costs, yielding better utilization of adjacent geometries. More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals, then uses the correspondence to propagate adjacent costs to the reference geometry, followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU, Tanks & Temple, and ETH3D datasets. Notably, our method ranks 1st on the Tanks & Temple Advanced benchmark.