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 ma...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61973301,61972020Youth Innovation Promotion Association CAS。
文摘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.
