MLV: Robust Image Matching via Multilayer Verification

Image matching is one of the hottest research topics in the field of computer vision, and it aims to compute the correct feature correspondences between images; however, under the influence of illumination changes and scale changes, relying only on the similarity between feature descriptors as the basis for judging feature matching tends to generate more false feature correspondences. To solve the above problems, in this article, we systematically analyze various factors that lead to false feature correspondences, design corresponding solutions for these various factors, and then propose a multilayer verification (MLV) image-matching method. First, eliminating the potential mismatches caused by brute force matching; second, eliminating the mismatches caused by the ambiguity of descriptors; and third, eliminating the feature matches that do not satisfy geometric constraints. Finally, we test the proposed MLV method on several open-source datasets and make a comprehensive comparison with the classical methods. Experimental results show that the MLV method not only can compute more true feature correspondences but also has a low computational cost. In addition, we applied the MLV method to sparse point cloud reconstruction to demonstrate its practical application value. Source code:
https://github.com/caomw/mlv
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