R-SLPR：基于区域的小到大点云配准对比学习框架

中文摘要 AI 辅助

点云（PC）配准是机器人系统中三维（3D）感知的基础，但经典配准算法在对齐包含有限、不完整或模糊几何线索的源点云与参考点云时表现不佳。将小型部分点云配准至大得多的全局参考点云的挑战在实际部署中普遍存在，而现有基于学习的方法通常假设尺度相当且存在显著重叠，对该问题的解决仍不足。为弥合这一差距，我们提出基于区域的小到大点云配准框架（R-SLPR），这是一种新颖的三阶段架构，将尺度不匹配的配准问题重新表述为区域提议、区域匹配和迭代细化的序列。与无法定位特定区域的传统方法不同，R-SLPR在估计刚性变换前明确识别候选区域，确保即使在严重尺度不匹配下也能实现鲁棒对齐。该框架引入斐波那契网格分割方法并结合对比学习目标，以有效生成和匹配局部几何块。在此基础上，新颖的级联锚点选择与细化算法迭代对齐源点云与目标区域，以最大化精度。在ModelNet40上的广泛评估表明，R-SLPR建立了新的最先进精度标准，优于现有方法，并将位置和旋转平均绝对误差（MAE）显著降至0.009和1.104。

英文摘要

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.

发表机构

• University of Washington（华盛顿大学）

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