HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
This addresses the problem of efficient and accurate 3D mapping for applications like autonomous driving, though it appears incremental as it builds on existing hierarchical and consensus-based methods.
The paper tackles the challenge of registering large-scale outdoor LiDAR point clouds by proposing HRegNet, a hierarchical network that uses keypoints and descriptors to achieve robust and precise registration, demonstrating high accuracy and efficiency in experiments on two datasets.
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.