Performance Evaluation of 3D Correspondence Grouping Algorithms

TL;DR

This paper thoroughly evaluates several 3D correspondence grouping algorithms across multiple benchmarks, analyzing their accuracy and efficiency in diverse real-world scenarios involving noise, clutter, and occlusion.

Contribution

It provides a comprehensive comparative analysis of existing algorithms' performance and efficiency in 3D correspondence grouping tasks across various challenging conditions.

Findings

Algorithms vary in precision and recall depending on noise and occlusion levels.

Some algorithms outperform others in specific scenarios like shape retrieval or point cloud registration.

Trade-offs between accuracy and computational efficiency are identified.

Abstract

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.