基于灵活分层细化与块描述符蒸馏的广义对应匹配
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation
AI总结：
针对现有最先进的即插即用对应匹配方法DFM的局限性，提出灵活最近邻分层细化与块描述符蒸馏方法，拓展骨干网络适配性并降低计算量，在多数据集上实现性能超越。
摘要：
对应匹配在众多机器人应用中发挥着关键作用。与传统的手工设计方法和近期的数据驱动方法相比，能够充分利用预训练骨干网络进行多尺度特征提取、并借助分层细化策略生成匹配对应关系的即插即用算法受到了广泛关注。本文的核心重点是解决深度特征匹配（DFM）这一当前最先进（SoTA）的即插即用对应匹配方法的局限性。首先，我们通过采用更灵活的最近邻搜索策略，去除了DFM分层细化过程中使用的预定义阈值，从而避免了在早期阶段排除重复但有效的匹配。我们的第二项技术贡献是整合了块描述符，这一设计拓展了DFM的适用范围，使其能够适配在各类计算机视觉任务（包括图像分类、语义分割和立体匹配）上预训练的多种骨干网络。考虑到我们的方法在实际机器人应用中的实用性，我们还提出了一种新颖的块描述符蒸馏策略，以进一步降低对应匹配的计算复杂度。在三个公开数据集上开展的大量实验证明了我们所提方法的优越性能。具体而言，在HPatches数据集上，该方法在1、3、5像素的容差下，平均匹配精度分别达到0.68、0.92和0.95，性能优于所有其他最先进算法。我们的源代码、演示视频和补充材料已公开发布于mias.group/GCM。
英文摘要：
Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at mias.group/GCM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
