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.

中文翻译：

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HCPM：分层候选修剪以实现高效的无检测器匹配

基于深度学习的图像匹配方法在计算机视觉中发挥着至关重要的作用，但它们往往面临大量的计算需求。为了应对这一挑战，我们提出了 HCPM，这是一种高效且无检测器的局部特征匹配方法，它采用分层修剪来优化匹配管道。与最近依赖于一组详尽的粗级候选者进行匹配的无检测器方法相比，HCPM 有选择地集中于信息丰富的候选者的简洁子集，从而减少了计算候选者并提高了匹配效率。该方法包括用于选择可靠候选的自修剪阶段和在粗略级别上识别相关补丁的交互式修剪阶段。我们的结果表明，HCPM 在速度方面显着超越现有方法，同时保持高精度。源代码将在发布后提供。