FastMAC: Stochastic Spectral Sampling of Correspondence Graph
Yifei Zhang Hao Zhao Hongyang Li Siheng Chen
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.