Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views

Robust line matching across wide-baseline views is a challenging task in computer vision. Most of the existing methods highly depend on the positional relationships between lines and the associated textures. These cues are sensitive to various image transformations especially perspective deformations, and likely to fail in the scenarios where few texture present. In this paper, we construct a new coplanar line-points invariant upon a newly developed projective invariant, named characteristic number, and propose a line matching algorithm using the invariant. The construction of this invariant uses intersections of coplanar lines instead of endpoints, rendering more robust matching across views. Additionally, a series of line-points invariant values generate the similarity metric for matching that is less affected by mismatched interest points than traditional approaches. Accurate homography recovered from the invariant allows all lines, even those without interest points around them, a chance to be matched. Extensive comparisons with the state-of-the-art validate the matching accuracy and robustness of the proposed method to projective transformations. The method also performs well for image pairs with few textures and similar textures.