  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.
