  Seeking consistent point-to-point correspondences between 3D rigid data
(point clouds, meshes, or depth maps) is a fundamental problem in 3D computer
vision. While a number of correspondence selection methods have been proposed
in recent years, their advantages and shortcomings remain unclear regarding
different applications and perturbations. To fill this gap, this paper gives a
comprehensive evaluation of nine state-of-the-art 3D correspondence grouping
methods. A good correspondence grouping algorithm is expected to retrieve as
many as inliers from initial feature matches, giving a rise in both precision
and recall as well as facilitating accurate transformation estimation. Toward
this rule, we deploy experiments on three benchmarks with different application
contexts including shape retrieval, 3D object recognition, and point cloud
registration together with various perturbations such as noise, point density
variation, clutter, occlusion, partial overlap, different scales of initial
correspondences, and different combinations of keypoint detectors and
descriptors. The rich variety of application scenarios and nuisances result in
different spatial distributions and inlier ratios of initial feature
correspondences, thus enabling a thorough evaluation. Based on the outcomes, we
give a summary of the traits, merits, and demerits of evaluated approaches and
indicate some potential future research directions.
