  We present a simple yet effective method for 3D correspondence grouping. The
objective is to accurately classify initial correspondences obtained by
matching local geometric descriptors into inliers and outliers. Although the
spatial distribution of correspondences is irregular, inliers are expected to
be geometrically compatible with each other. Based on such observation, we
propose a novel representation for 3D correspondences, dubbed compatibility
feature (CF), to describe the consistencies within inliers and inconsistencies
within outliers. CF consists of top-ranked compatibility scores of a candidate
to other correspondences, which purely relies on robust and rotation-invariant
geometric constraints. We then formulate the grouping problem as a
classification problem for CF features, which is accomplished via a simple
multilayer perceptron (MLP) network. Comparisons with nine state-of-the-art
methods on four benchmarks demonstrate that: 1) CF is distinctive, robust, and
rotation-invariant; 2) our CF-based method achieves the best overall
performance and holds good generalization ability.
