SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration

In this paper, we present a second order spatial compat-ibility (SC2) measure based method for efficient and robust point cloud registration (PCR), called SC2-PCR 1. Firstly, we propose a second order spatial compatibility (SC2) mea-sure to compute the similarity between correspondences. It considers the global compatibility instead of local consis-tency, allowing for more distinctive clustering between in-liers 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 SC2 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 ef-ficient and robust. In addition, the proposed SC2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to in-vestigate the performance of our method.