Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching

• URL: http://arxiv.org/abs/2512.01850v1
• Date: Mon, 01 Dec 2025 16:36:51 GMT
• Title: Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching
• Authors: Yue Pan, Tao Sun, Liyuan Zhu, Lucas Nunes, Iro Armeni, Jens Behley, Cyrill Stachniss,
• Abstract summary: Point cloud registration is a core step for 3D reconstruction and robot localization.<n>We cast registration as conditional generation: a learned continuous, point-wise velocity field transports noisy points to a registered scene.<n>Our approach achieves state-of-the-art results on pairwise and multi-view registration benchmarks.
• Score: 41.014082260379304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud registration aligns multiple unposed point clouds into a common frame, and is a core step for 3D reconstruction and robot localization. In this work, we cast registration as conditional generation: a learned continuous, point-wise velocity field transports noisy points to a registered scene, from which the pose of each view is recovered. Unlike previous methods that conduct correspondence matching to estimate the transformation between a pair of point clouds and then optimize the pairwise transformations to realize multi-view registration, our model directly generates the registered point cloud. With a lightweight local feature extractor and test-time rigidity enforcement, our approach achieves state-of-the-art results on pairwise and multi-view registration benchmarks, particularly with low overlap, and generalizes across scales and sensor modalities. It further supports downstream tasks including relocalization, multi-robot SLAM, and multi-session map merging. Source code available at: https://github.com/PRBonn/RAP.

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