提出一种兼顾几何一致性与局部引导的点云配准方法，提升实时性与精度。
A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration
- 通过局部引导注意力避免无关区域干扰，增强特征匹配聚焦性。
- 在多个数据集上达到顶尖精度，且推理速度优于现有方法。
- 适合机器人里程计等需高效高鲁棒性的实时点云应用。
基于深度学习的特征匹配在无初始位姿先验条件下展现出显著优势。尽管粗粒度到细粒度的匹配策略普遍应用，但现有方法的粗匹配通常稀疏且缺乏几何一致性考量，导致后续细匹配依赖低效的最优传输或假设-选择机制。为此，本文提出一致性感知的局部引导变压器（CAST），引入局部引导交叉注意力模块以避开无关区域干扰，并设计一致性感知自注意力模块，利用几何一致的对应关系增强匹配能力。此外，轻量级细匹配模块可同时处理稀疏关键点与密集特征，实现精准变换估计。在多个室外激光雷达与室内RGBD点云数据集上的大量实验表明，该方法在准确性、效率和鲁棒性方面均达到当前最优水平。
原文摘要 · Abstract (English)
Deep learning-based feature matching has shown great superiority for point cloud registration in the absence of pose priors. Although coarse-to-fine matching approaches are prevalent, the coarse matching of existing methods is typically sparse and loose without consideration of geometric consistency, which makes the subsequent fine matching rely on ineffective optimal transport and hypothesis-and-selection methods for consistency. Therefore, these methods are neither efficient nor scalable for real-time applications such as odometry in robotics. To address these issues, we design a consistency-aware spot-guided Transformer (CAST), which incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas, and a consistency-aware self-attention module to enhance matching capabilities with geometrically consistent correspondences. Furthermore, a lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately. Extensive experiments on both outdoor LiDAR point cloud datasets and indoor RGBD point cloud datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness.
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