R-SLPR: Region-based Small-to-Large Point-cloud Registration with Contrastive Learning

• URL: http://arxiv.org/abs/2607.26583v1
• Date: Wed, 29 Jul 2026 08:01:31 GMT
• Title: R-SLPR: Region-based Small-to-Large Point-cloud Registration with Contrastive Learning
• Abstract summary: Region-based Small-to-Large Point-cloud Registra- tion framework (R-SLPR)<n>R-SLPR is a novel three-stage architecture that reformulates the scale-mismatched registration problem into a sequence of region proposal, regional matching, and iterative refinement.<n>It establishes a new state-of-the-art accuracy standard, outperforming prior approaches and significantly reducing position and rotation Mean Absolute Error (MAE) to 0.009 and 1.104, respectively.
• Score: 15.14722466963284
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Point-cloud (PC) registration is fundamental to three-dimensional (3D) perception in robotic systems. However, classic registration algorithms falter when aligning a source PC containing limited, incomplete, or ambiguous geometric cues against a reference. This challenge of registering a small, partial PC to a significantly larger global reference is pervasive in real-world deployment yet remains insufficiently addressed by existing learning-based approaches, which typically assume comparable scales and significant overlap. To bridge this gap, we propose the Region-based Small-to-Large Point-cloud Registra- tion framework (R-SLPR), a novel three-stage architecture that fundamentally reformulates the scale-mismatched registration problem into a sequence of region proposal, regional matching, and iterative refinement. Unlike conventional methods that fail to localize specific regions, R-SLPR explicitly identifies candidate regions prior to estimating rigid transformations, ensuring robust alignment even under severe scale mismatch. The framework introduces a Fibonacci Grid Segmentation method coupled with a contrastive learning objective to effectively generate and match local geometric patches. Building on this, a novel Cascade Anchor Selection and Refinement algorithm iteratively aligns the source with the target region to maximize precision. Extensive evaluation on ModelNet40 demonstrates that R-SLPR establishes a new state-of-the-art accuracy standard, outperforming prior approaches and significantly reducing position and rotation Mean Absolute Error (MAE) to 0.009 and 1.104, respectively.

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