Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search

• URL: http://arxiv.org/abs/2404.06155v1
• Date: Tue, 9 Apr 2024 09:28:05 GMT
• Title: Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
• Authors: Tianyu Huang, Haoang Li, Liangzu Peng, Yinlong Liu, Yun-Hui Liu,
• Abstract summary: Estimating rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. This paper proposes a parameter search strategy to accelerate the search while maintaining high robustness. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples.
• Score: 44.774302677330105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios ($>$ 95 $\%$ is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost.

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