# Investigating Spherical Epipolar Rectification for Multi-View Stereo 3D Reconstruction

arXiv: https://arxiv.org/abs/2204.04141 · HTML: https://mlsanity.com/paper/2204.04141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo](https://mlsanity.com/paper/2309.09379/) (2023)
- [Spherical-to-ERP Epipolar Rectification for Single-Axis Disparity in 360 Stereo](https://mlsanity.com/paper/2606.24847/) (2026)

## Abstract (arXiv)

Multi-view stereo (MVS) reconstruction is essential for creating 3D models. The approach involves applying epipolar rectification followed by dense matching for disparity estimation. However, existing approaches face challenges in applying dense matching for images with different viewpoints primarily due to large differences in object scale. In this paper, we propose a spherical model for epipolar rectification to minimize distortions caused by differences in principal rays. We evaluate the proposed approach using two aerial-based datasets consisting of multi-camera head systems. We show through qualitative and quantitative evaluation that the proposed approach performs better than frame-based epipolar correction by enhancing the completeness of point clouds by up to 4.05% while improving the accuracy by up to 10.23% using LiDAR data as ground truth.