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

TL;DR

This paper introduces a spherical epipolar rectification model for multi-view stereo reconstruction, reducing distortions from viewpoint differences and improving 3D point cloud quality.

Contribution

The paper proposes a novel spherical model for epipolar rectification that outperforms traditional frame-based methods in multi-view stereo reconstruction.

Findings

Enhanced point cloud completeness by up to 4.05%

Improved accuracy by up to 10.23% using LiDAR ground truth

Better handling of viewpoint differences in multi-camera systems

Abstract

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.