  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.
