一种用于通用与层次化点云配准的一致性感知斑点引导 Transformer
A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration
AI总结：
针对点云配准中粗匹配缺乏几何一致性导致效率与可扩展性差的问题，提出一致性感知斑点引导Transformer（CAST），通过斑点引导交叉注意力与一致性感知自注意力模块及轻量级细匹配模块，实现了最先进的配准精度、效率和鲁棒性。
摘要：
基于深度学习的特征匹配在缺乏位姿先验的点云配准中展现出巨大优势。尽管由粗到细的匹配方法十分普遍，但现有方法的粗匹配通常稀疏且松散，未考虑几何一致性，这使得后续的细匹配依赖无效的最优传输和假设与选择方法来保持一致性。因此，这些方法对于机器人里程计等实时应用既不高效也不可扩展。为解决这些问题，我们设计了一种一致性感知斑点引导 Transformer（CAST），它包含一个斑点引导交叉注意力模块以避免干扰无关区域，以及一个一致性感知自注意力模块以利用几何一致的对应关系增强匹配能力。此外，一个适用于稀疏关键点和密集特征的轻量级细匹配模块可以准确估计变换。在室外 LiDAR 点云数据集和室内 RGBD 点云数据集上的大量实验表明，我们的方法实现了最先进的准确性、效率和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
