  Registering urban point clouds is a quite challenging task due to the
large-scale, noise and data incompleteness of LiDAR scanning data. In this
paper, we propose SARNet, a novel semantic augmented registration network aimed
at achieving efficient registration of urban point clouds at city scale.
Different from previous methods that construct correspondences only in the
point-level space, our approach fully exploits semantic features as assistance
to improve registration accuracy. Specifically, we extract per-point semantic
labels with advanced semantic segmentation networks and build a prior semantic
part-to-part correspondence. Then we incorporate the semantic information into
a learning-based registration pipeline, consisting of three core modules: a
semantic-based farthest point sampling module to efficiently filter out
outliers and dynamic objects; a semantic-augmented feature extraction module
for learning more discriminative point descriptors; a semantic-refined
transformation estimation module that utilizes prior semantic matching as a
mask to refine point correspondences by reducing false matching for better
convergence. We evaluate the proposed SARNet extensively by using real-world
data from large regions of urban scenes and comparing it with alternative
methods. The code is available at
https://github.com/WinterCodeForEverything/SARNet.
