  LiDAR-based 3D detection has made great progress in recent years. However,
the performance of 3D detectors is considerably limited when deployed in unseen
environments, owing to the severe domain gap problem. Existing domain adaptive
3D detection methods do not adequately consider the problem of the
distributional discrepancy in feature space, thereby hindering generalization
of detectors across domains. In this work, we propose a novel unsupervised
domain adaptive \textbf{3D} detection framework, namely \textbf{G}eometry-aware
\textbf{P}rototype \textbf{A}lignment (\textbf{GPA-3D}), which explicitly
leverages the intrinsic geometric relationship from point cloud objects to
reduce the feature discrepancy, thus facilitating cross-domain transferring.
Specifically, GPA-3D assigns a series of tailored and learnable prototypes to
point cloud objects with distinct geometric structures. Each prototype aligns
BEV (bird's-eye-view) features derived from corresponding point cloud objects
on source and target domains, reducing the distributional discrepancy and
achieving better adaptation. The evaluation results obtained on various
benchmarks, including Waymo, nuScenes and KITTI, demonstrate the superiority of
our GPA-3D over the state-of-the-art approaches for different adaptation
scenarios. The MindSpore version code will be publicly available at
\url{https://github.com/Liz66666/GPA3D}.
