  Camera-based person re-identification (ReID) systems have been widely applied
in the field of public security. However, cameras often lack the perception of
3D morphological information of human and are susceptible to various
limitations, such as inadequate illumination, complex background, and personal
privacy. In this paper, we propose a LiDAR-based ReID framework, ReID3D, that
utilizes pre-training strategy to retrieve features of 3D body shape and
introduces Graph-based Complementary Enhancement Encoder for extracting
comprehensive features. Due to the lack of LiDAR datasets, we build LReID, the
first LiDAR-based person ReID dataset, which is collected in several outdoor
scenes with variations in natural conditions. Additionally, we introduce
LReID-sync, a simulated pedestrian dataset designed for pre-training encoders
with tasks of point cloud completion and shape parameter learning. Extensive
experiments on LReID show that ReID3D achieves exceptional performance with a
rank-1 accuracy of 94.0, highlighting the significant potential of LiDAR in
addressing person ReID tasks. To the best of our knowledge, we are the first to
propose a solution for LiDAR-based ReID. The code and datasets will be released
soon.
