GMOR: A Lightweight Robust Point Cloud Registration Framework via Geometric Maximum Overlapping

TL;DR

This paper introduces GMOR, a lightweight and robust point cloud registration framework that uses geometric maximum overlapping and rotation-only branch-and-bound search to improve accuracy and efficiency, especially under high outlier ratios.

Contribution

The paper proposes a novel registration framework that decomposes transformations via Chasles' theorem and employs a rotation-only BnB search with RMQ problems, reducing complexity and improving robustness.

Findings

Superior accuracy on indoor and outdoor datasets

Polynomial time complexity with linear space growth

Effective handling of high outlier ratios

Abstract

Point cloud registration based on correspondences computes the rigid transformation that maximizes the number of inliers constrained within the noise threshold. Current state-of-the-art (SOTA) methods employing spatial compatibility graphs or branch-and-bound (BnB) search mainly focus on registration under high outlier ratios. However, graph-based methods require at least quadratic space and time complexity for graph construction, while multi-stage BnB search methods often suffer from inaccuracy due to local optima between decomposed stages. This paper proposes a geometric maximum overlapping registration framework via rotation-only BnB search. The rigid transformation is decomposed using Chasles' theorem into a translation along rotation axis and a 2D rigid transformation. The optimal rotation axis and angle are searched via BnB, with residual parameters formulated as range maximum query (RMQ) problems. Firstly, the top-k candidate rotation axes are searched within a hemisphere parameterized by cube mapping, and the translation along each axis is estimated through interval stabbing of the correspondences projected onto that axis. Secondly, the 2D registration is relaxed to 1D rotation angle search with 2D RMQ of geometric overlapping for axis-aligned rectangles, which is solved deterministically in polynomial time using sweep line algorithm with segment tree. Experimental results on indoor 3DMatch/3DLoMatch scanning and outdoor KITTI LiDAR datasets demonstrate superior accuracy and efficiency over SOTA methods, while the time complexity is polynomial and the space complexity increases linearly with the number of points, even in the worst case.