Outlier Robust Feature Correspondence by Learning Based Matching Process

Feature correspondence is a crucial aspect of various computer vision and robot vision tasks. Unlike traditional optimization-based matching techniques, researchers have recently adopted a learning-based approach for matching, but these methods face challenges in dealing with outlier features. This paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier issue. Additionally, the authors introduce a modified cross-entropy matching loss to handle the outlier problem. As a result, the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier features. Benchmark experiments confirm the effectiveness of the proposed approach.