A Triangulation-based Hierarchical Image Matching Method for Wide-Baseline Images

Abstract

This paper presents a triangulation-based hierarchical image matching method for wide-baseline images. The method includes the following three steps: (a) image orientation by incorporating the SIFT algorithm with the RANSAC approach, (b) feature matching based on the self-adaptive triangle constraint, which includes point-to-point matching and subsequent point-to-area matching, and (c) triangulation constrained dense matching based on the previous matched results. Two new constraints, the triangulation-based disparity constraint and triangulation-based gradient orientation constraint, are developed to alleviate the matching ambiguity for wide-baseline images. A triangulation based affine-adaptive cross-correlation is developed to help find correct matches even in the image regions with large perspective distortions. Experiments using Mars ground wide-baseline images and terrestrial wide-baseline images revealed that the proposed method is capable of generating reliable and dense matching results for terrain mapping and surface reconstruction from the wide-baseline images.