In this paper, we present a second order spatial compatibility (SC^2) measure
based method for efficient and robust point cloud registration (PCR), called
SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2)
measure to compute the similarity between correspondences. It considers the
global compatibility instead of local consistency, allowing for more
distinctive clustering between inliers and outliers at early stage. Based on
this measure, our registration pipeline employs a global spectral technique to
find some reliable seeds from the initial correspondences. Then we design a
two-stage strategy to expand each seed to a consensus set based on the SC^2
measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm
to generate a candidate rigid transformation and select the best model as the
final result. Our method can guarantee to find a certain number of outlier-free
consensus sets using fewer samplings, making the model estimation more
efficient and robust. In addition, the proposed SC^2 measure is general and can
be easily plugged into deep learning based frameworks. Extensive experiments
are carried out to investigate the performance of our method. Code will be
available at https://github.com/ZhiChen902/SC2-PCR.

本文提出了一种基于空间兼容性 (SC2) 的点云配准方法 (SC2-PCR)，使用二阶空间兼容性 (SC2) 度量来计算对应关系的相似度，运用全局光谱技术找到可靠种子点并使用二阶空间兼容性 (SC2) 矩阵来设计一个两阶段的策略扩展每个种子点的一致性集，然后使用加权奇异值分解算法将每个一致性集提供给生成候选刚性变换，并选择最佳模型作为最终结果，能够保证使用更少的采样找到一定数量的无离群值的一致性集，使模型估计更有效和健壮。