A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching
This work addresses the need for clear performance insights into 3D correspondence grouping methods for researchers and practitioners in 3D computer vision, though it is incremental as it focuses on evaluation rather than proposing new methods.
The paper conducted a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods across three benchmarks with various perturbations, revealing their traits, merits, and demerits to clarify performance in different applications.
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.