Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hierarchical neural network with double attention named HDMNet is proposed for large-scale outdoor LiDAR point cloud registration. Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance. Moreover, in order to further utilize the sparse matching information from deeper layer, we develop a novel trainable embedding mask to incorporate the confidence scores of correspondences obtained from pose estimation of deeper layer, eliminating additional computations. The high-confidence keypoints in the sparser point cloud of the deeper layer correspond to a high-confidence spatial neighborhood region in shallower layer, which will receive more attention, while the features of non-key regions will be masked. Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HDMNet.

翻译：室外LiDAR点云通常规模庞大且分布复杂。为实现高效精准的配准，强调局部区域间的相似性并优先进行全局局部到局部的匹配至关重要，随后可通过低成本的精细配准提升精度。本文提出了一种名为HDMNet的新型双注意力层次神经网络，用于大规模室外LiDAR点云配准。具体而言，我们引入了一种新颖的特征一致性增强双软匹配网络，以块到块的方式实现高灵活性的两阶段匹配，同时高效扩大感受野，显著提升配准性能。此外，为进一步利用深层稀疏匹配信息，我们开发了一种新型可训练嵌入掩码，将根据深层位姿估计获得的对应点置信度分数融入其中，无需额外计算。深层更稀疏点云中的高置信度关键点对应浅层中的高置信度空间邻域区域，这些区域将获得更多关注，而非关键区域的特征则被屏蔽。在两个大规模室外LiDAR点云数据集上的广泛实验证明了所提出的HDMNet的高精度与高效率。