Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus

Establishing dense correspondences across semantically similar images is a challenging task, due to the large intra-class variation caused by the unconstrained setting of images, which is prone to cause matching errors. To suppress potential matching ambiguity, NCNet explores the neighborhood consensus pattern in the 4D space of all possible correspondences, which is based on the assumption that the correspondence is continuous in space. We retain the neighborhood consensus constraint, while introducing semantic segmentation information into the features, which makes them more distinguishable and reduces matching ambiguity from a feature perspective. Specifically, we combine the semantic segmentation network to extract semantic features and the 4D convolution to explore 4D-space context consistency. Experiments demonstrate that our algorithm has good semantic matching performances and semantic segmentation information can improve semantic matching accuracy.

Automated summary

The algorithm enhances semantic matching accuracy by exploring neighborhood consensus patterns in 4D space and introducing semantic segmentation information to distinguish features better and reduce matching ambiguity.