  Point cloud registration is a fundamental problem in 3D computer vision.
Outdoor LiDAR point clouds are typically large-scale and complexly distributed,
which makes the registration challenging. In this paper, we propose an
efficient hierarchical network named HRegNet for large-scale outdoor LiDAR
point cloud registration. Instead of using all points in the point clouds,
HRegNet performs registration on hierarchically extracted keypoints and
descriptors. The overall framework combines the reliable features in deeper
layer and the precise position information in shallower layers to achieve
robust and precise registration. We present a correspondence network to
generate correct and accurate keypoints correspondences. Moreover, bilateral
consensus and neighborhood consensus are introduced for keypoints matching and
novel similarity features are designed to incorporate them into the
correspondence network, which significantly improves the registration
performance. Besides, the whole network is also highly efficient since only a
small number of keypoints are used for registration. Extensive experiments are
conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate
the high accuracy and efficiency of the proposed HRegNet. The project website
is https://ispc-group.github.io/hregnet.
