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.

提出了一种用于大规模室外 LiDAR 点云注册的新型分层神经网络，该网络名为 HDMNet，通过引入增强的特征一致性双软匹配网络来实现具有高灵活性的两阶段匹配，并在以补丁为单位的方式下，扩大感受野并提高效率，从而显著提高了注册性能。此外，还开发了一种新颖的可训练嵌入遮罩，以整合由于较深层次姿态估计获得的对应关系的置信度得分，从而消除了额外的计算。在两个大规模室外 LiDAR 点云数据集上进行了广泛实验，以展示提出的 HDMNet 的高准确性和高效性。