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.

基于深度学习的图像匹配方法在计算机视觉中发挥了重要作用，但往往需要大量的计算资源。为了解决这个问题，我们提出了一种高效且无需检测器的局部特征匹配方法 HCPM，通过分层修剪来优化匹配流程。与依赖于粗略级别候选集进行匹配的最新无检测器方法不同，HCPM 仅集中在一小部分有信息量的候选项上，从而减少计算要求并提高匹配效率。该方法包括自剪枝阶段以选择可靠的候选项，以及在粗略级别上识别相关补丁的交互式剪枝阶段。我们的结果表明，HCPM 在速度和高准确性方面显著超越现有方法。发布后，源代码将可供使用。