# Performance Evaluation of 3D Correspondence Grouping Algorithms

arXiv: https://arxiv.org/abs/1804.02085 · HTML: https://mlsanity.com/paper/1804.02085/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [Skeleton Merger: an Unsupervised Aligned Keypoint Detector](https://mlsanity.com/paper/2103.10814/) (2021)
- [PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration](https://mlsanity.com/paper/2209.00219/) (2022)
- [Stress-Testing Point Cloud Registration on Automotive LiDAR](https://mlsanity.com/paper/2204.07719/) (2022)

## Abstract (arXiv)

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.