PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registration

2026-09-21

AI Summary

PARTE方法通过利用平面结构作为补充注册证据，结合点和平面对应关系进行全局点云配准，解决了因重叠区域有限、重复几何和传感器噪声导致的外点问题。

Abstract

Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated by outliers. Planar regions are particularly difficult for conventional point descriptors and are therefore often suppressed or discarded before matching. We present PARTE (Plane-Assisted Robust Transformation Estimation), a global registration method that instead treats planar structure as complementary registration evidence. PARTE extracts planar patches and represents them using our novel Plane Context Histogram (PCH), a descriptor that encodes the geometry surrounding each patch, while a two-level matching procedure identifies reliable plane correspondences. Candidate point and plane correspondences are combined in a confidence-weighted compatibility graph for joint outlier rejection, followed by rigid transformation estimation. When no usable plane correspondences are available, PARTE naturally reduces to point-only registration. We evaluate PARTE on 8,097 registration pairs across six indoor and outdoor benchmarks spanning dense RGB-D and sparse LiDAR measurements. Evaluations show PARTE achieves the highest overall success rate against 13 standard and state-of-the-art methods while maintaining low runtime. An open-source C++ implementation with Python bindings is provided at https://parte.pages.dev.

Problem

• point cloud registration
• outliers
• planar regions

Innovation

• Plane-Assisted
• Robust Transformation Estimation
• Plane Context Histogram (PCH)
• Two-Level Matching Procedure
• Confidence-Weighted Compatibility Graph