[Submitted on 20 Oct 2026]
Title:Register Anything Model for Generalizable and Robust Point Cloud Registration
View a PDF of the paper titled Register Anything Model for Generalizable and Robust Point Cloud Registration, by Zheng Qin and 7 other authors
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Abstract:We present Register Anything Model (RAM), a unified model for generalizable 3D point cloud registration that robustly estimates 6-DoF transformations across diverse domains spanning sensors and environments. Existing registration methods are typically tailored to a single domain, with carefully tuned architectures and hyperparameters, and thus degrade markedly when applied to novel domains. We bridge this gap by training a single model on diverse data from indoor, outdoor, and object domains. A central challenge is the severe geometric discrepancy across domains, i.e., differences in scene scale, sensor noise, point density, and structural patterns, which hinders processing with a unified point-based model. To address this, we introduce a geometry-aware normalization strategy that jointly voxelizes and normalizes point clouds into a standardized geometric space, adaptively based on their geometric properties to preserve salient local structures while standardizing resolution. Built on this normalization, RAM follows a hierarchical coarse-to-fine paradigm, and employs an efficient geometric transformer that models local geometric consistency instead of globally over all points. This design mitigates discretization noise introduced by voxelization, and improves computational efficiency. Extensive experiments on 12 benchmarks demonstrate state-of-the-art performance in terms of inlier ratio and registration recall. Notably, our method outperforms the counterparts trained on each separate domain, showing superior generalization capability.
Comments: Accepted by NeurIPS 2026. Code and models are available at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.28821 [cs.CV]
(or arXiv:2610.28821v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.28821
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zheng Qin [view email] [v1] Tue, 20 Oct 2026 18:40:50 UTC (10,590 KB)
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