Feature Matching via Motion-Consistency Driven Probabilistic Graphical Model

This paper proposes an effective method, termed as motion-consistency driven matching (MCDM), for mismatch removal from given tentative correspondences between two feature sets. In particular, we regard each correspondence as a hypothetical node, and formulate the matching problem into a probabilistic graphical model to infer the state of each node (e.g., true or false correspondence). By investigating the motion consistency of true correspondences, a general prior is incorporated into our formulation to differentiate false correspondences from the true ones. The final inference is casted into an integer quadratic programming problem, and the solution is obtained by using an efficient optimization technique based on the Frank-Wolfe algorithm. Extensive experiments on general feature matching, as well as fundamental matrix estimation, relative pose estimation and loop-closure detection, demonstrate that our MCDM possesses strong generalization ability as well as high accuracy, which outperforms state-of-the-art methods. Meanwhile, due to the low computational complexity, the proposed method is efficient for practical feature matching tasks.

Automated summary

This paper proposes a method called motion-consistency driven matching (MCDM) to remove mismatches between two feature sets. By formulating the matching problem into a probabilistic graphical model and incorporating motion consistency and a general prior, the proposed method can effectively differentiate false correspondences from the true ones. Experimental results demonstrate that MCDM has strong generalization ability and high accuracy, outperforming state-of-the-art methods. Additionally, the proposed method has low computational complexity and is efficient for practical feature matching tasks.