FastMAC: 对应图的随机谱采样
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
AI总结：
针对三维点云配准中的对应图，提出基于随机谱采样的FastMAC算法，在保持高成功率的同时实现实时速度，加速MAC达80倍。
摘要：
三维对应关系，即一对三维点，是计算机视觉中的一个基本概念。一组三维对应关系，在配备兼容性边后，构成一个对应图。该图是若干最先进的三维点云配准方法中的关键组成部分，例如基于最大团（MAC）的方法。然而，其性质尚未被充分理解。因此，我们提出了首个将图信号处理引入对应图领域的研究。我们利用对应图上的广义度信号，并追求保留该信号高频分量的采样策略。为了解决确定性采样中耗时的奇异值分解问题，我们采用了一种随机近似采样策略。因此，我们方法的核心是对应图的随机谱采样。作为应用，我们构建了一个完整的3D配准算法，称为FastMAC，该算法达到实时速度，同时性能下降极少。通过大量实验，我们验证了FastMAC适用于室内和室外基准。例如，在KITTI上，FastMAC可以将MAC加速80倍，同时保持较高的配准成功率。代码公开于https://github.com/Forrest-110/FastMAC。
英文摘要：
3D correspondence, i.e., a pair of 3D points, is a fundamental concept in computer vision. A set of 3D correspondences, when equipped with compatibility edges, forms a correspondence graph. This graph is a critical component in several state-of-the-art 3D point cloud registration approaches, e.g., the one based on maximal cliques (MAC). However, its properties have not been well understood. So we present the first study that introduces graph signal processing into the domain of correspondence graph. We exploit the generalized degree signal on correspondence graph and pursue sampling strategies that preserve high-frequency components of this signal. To address time-consuming singular value decomposition in deterministic sampling, we resort to a stochastic approximate sampling strategy. As such, the core of our method is the stochastic spectral sampling of correspondence graph. As an application, we build a complete 3D registration algorithm termed as FastMAC, that reaches real-time speed while leading to little to none performance drop. Through extensive experiments, we validate that FastMAC works for both indoor and outdoor benchmarks. For example, FastMAC can accelerate MAC by 80 times while maintaining high registration success rate on KITTI. Codes are publicly available at https://github.com/Forrest-110/FastMAC.
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