【摘要】 点云配准是点云数据处理的一项重要的基础技术，广泛应用于机器人、工业检测等领域。然而，点云特征缺乏全局上下文信息以及点对特征误匹配使得刚体变换矩阵解算不准确，导致点云配准精度低。针对上述问题，提出一种融合全局感知和跨点云交互的点云配准方法，其主要包含全局-局部特征提取模块和注意力特征融合匹配模块。全局-局部特征提取模块可以提取点云的局部几何特征并结合全局上下文信息，以此提高点云特征的全局一致性。注意力特征融合匹配模块通过交叉注意力机制融合两片点云的特征信息，并获取点对匹配关系的置信度，通过加权奇异值分解算法提高了变换矩阵的解算精度。实验结果表明，所提方法在3DMatch数据集上得到的特征匹配召回率(feature match recall, FMR)达到97.9%，配准召回率达到90.5%，优于现有的方法。更多还原

【Abstract】 Point cloud registration is a fundamental technique in point cloud data processing and finds widespread applications in fields such as robotics and industrial inspection. However, the lack of global contextual information in point cloud features and the presence of feature mismatches between point pairs often result in inaccurate rigid transformation matrix estimation, leading to low registration accuracy. To address these challenges, this paper proposes a point cloud registration method integrating global context awareness and cross-point-cloud interactions.The method comprises two primary modules: a global-local feature extraction module and an attention-based feature fusion and matching module. The global-local feature extraction module extracts local geometric features from the point cloud while incorporating global contextual information to enhance the global consistency of point cloud features. The attention-based feature fusion and matching module employs a cross-attention mechanism to integrate feature information from two point clouds and estimates the confidence of point-pair correspondences.Then, a weighted singular value decomposition algorithm was implemented to improve the estimation accuracy of the rigid transformation matrix. Experimental results show that the proposed method achieves a feature matching recall(FMR) of 97.9% on the 3 DMatch dataset, surpassing existing methods.更多还原