1-point sample consensus on correspondence set for 3D point cloud registration

Siwen Quan; Yongfeng Ju; Yuxin Cheng; Meng Hui; Lin Bai

doi:10.1117/12.2623196

16 February 2022 1-point sample consensus on correspondence set for 3D point cloud registration

Siwen Quan, Yongfeng Ju, Yuxin Cheng, Meng Hui, Lin Bai

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831K (2022) https://doi.org/10.1117/12.2623196
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Estimating a six-degree-of-freedom pose from a set of correspondences remains a popular solution for 3D point cloud registration. The random sample consensus (RANSAC) method is a typical pose estimator for this task. However, RANSAC still suffers from several limitations including low efficiency and the sensitivity to high outlier ratios. To tackle these problems, we propose a 1-point sample consensus method. It first constructs a local reference frame for the keypoint based on multi-scale normal vectors, which allows our method to exhibit a linear time complexity. Then, we propose a novel hypothesis evaluation method that concentrates on accurate inliers and is more reliable for hypothesis evaluation. With comparisons with two RANSAC-like methods, our method manages to achieve more accurate and efficient registrations, making it a good gift for practical applications.

Citation Download Citation

Siwen Quan, Yongfeng Ju, Yuxin Cheng, Meng Hui, and Lin Bai "1-point sample consensus on correspondence set for 3D point cloud registration", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831K (16 February 2022); https://doi.org/10.1117/12.2623196

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