Geometric verification using semi-2D constraints for 3D object retrieval

Abstract

Geometric verification with epipolar geometry often results in a high score for an incorrect image pair due to ambiguity in its geometric constraints. The ambiguity is caused by a high degree of freedom in the epipolar geometry and a weak constraint from the fitting between a point and a line. In order to mitigate the ambiguity, we propose to filter geometrically inconsistent components, namely correspondences, a sample, a model, and inliers in a RANSAC-based geometric verification. For the filtering, we introduce novel semi-2D constraints whose geometric constraint is weaker than full-2D constraint, but stronger than pure-epipolar constraint. Additionally, an advantage of the proposed approach is that it requires only an image pair instead of neither additional information nor prior learning. Experiments on the public dataset containing 3D object images show that the proposed approach improves the true positive rate when the false positive rate is low, and greatly reduces computational time for the geometric verification of both a correct image pair and an incorrect image pair.