  For many driving safety applications, it is of great importance to accurately
register LiDAR point clouds generated on distant moving vehicles. However, such
point clouds have extremely different point density and sensor perspective on
the same object, making registration on such point clouds very hard. In this
paper, we propose a novel feature extraction framework, called APR, for online
distant point cloud registration. Specifically, APR leverages an autoencoder
design, where the autoencoder reconstructs a denser aggregated point cloud with
several frames instead of the original single input point cloud. Our design
forces the encoder to extract features with rich local geometry information
based on one single input point cloud. Such features are then used for online
distant point cloud registration. We conduct extensive experiments against
state-of-the-art (SOTA) feature extractors on KITTI and nuScenes datasets.
Results show that APR outperforms all other extractors by a large margin,
increasing average registration recall of SOTA extractors by 7.1% on LoKITTI
and 4.6% on LoNuScenes. Code is available at https://github.com/liuQuan98/APR.
