HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free
  Matching
Ying Chen Yong Liu Kai Wu Qiang Nie Shang Xu Huifang Ma Bing Wang Chengjie Wang
Deep learning-based image matching methods play a crucial role in computer
vision, yet they often suffer from substantial computational demands. To tackle
this challenge, we present HCPM, an efficient and detector-free local
feature-matching method that employs hierarchical pruning to optimize the
matching pipeline. In contrast to recent detector-free methods that depend on
an exhaustive set of coarse-level candidates for matching, HCPM selectively
concentrates on a concise subset of informative candidates, resulting in fewer
computational candidates and enhanced matching efficiency. The method comprises
a self-pruning stage for selecting reliable candidates and an
interactive-pruning stage that identifies correlated patches at the coarse
level. Our results reveal that HCPM significantly surpasses existing methods in
terms of speed while maintaining high accuracy. The source code will be made
available upon publication.