  Domain adaptation for Cross-LiDAR 3D detection is challenging due to the
large gap on the raw data representation with disparate point densities and
point arrangements. By exploring domain-invariant 3D geometric characteristics
and motion patterns, we present an unsupervised domain adaptation method that
overcomes above difficulties. First, we propose the Spatial Geometry Alignment
module to extract similar 3D shape geometric features of the same object class
to align two domains, while eliminating the effect of distinct point
distributions. Second, we present Temporal Motion Alignment module to utilize
motion features in sequential frames of data to match two domains. Prototypes
generated from two modules are incorporated into the pseudo-label reweighting
procedure and contribute to our effective self-training framework for the
target domain. Extensive experiments show that our method achieves
state-of-the-art performance on cross-device datasets, especially for the
datasets with large gaps captured by mechanical scanning LiDARs and solid-state
LiDARs in various scenes. Project homepage is at
https://github.com/4DVLab/CL3D.git
