  This paper presents a thorough evaluation of several widely-used 3D
correspondence grouping algorithms, motived by their significance in vision
tasks relying on correct feature correspondences. A good correspondence
grouping algorithm is desired to retrieve as many as inliers from initial
feature matches, giving a rise in both precision and recall. Towards this rule,
we deploy the experiments on three benchmarks respectively addressing shape
retrieval, 3D object recognition and point cloud registration scenarios. The
variety in application context brings a rich category of nuisances including
noise, varying point densities, clutter, occlusion and partial overlaps. It
also results to different ratios of inliers and correspondence distributions
for comprehensive evaluation. Based on the quantitative outcomes, we give a
summarization of the merits/demerits of the evaluated algorithms from both
performance and efficiency perspectives.
