Investigating Spherical Epipolar Rectification for Multi-View Stereo 3D Reconstruction
This work addresses a domain-specific problem for aerial-based 3D reconstruction, offering incremental improvements over existing methods.
The paper tackled the problem of distortions in multi-view stereo 3D reconstruction caused by differences in object scale and viewpoints by proposing a spherical model for epipolar rectification, resulting in up to 4.05% improvement in point cloud completeness and up to 10.23% improvement in accuracy compared to frame-based methods.
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.