  Vision-centric bird-eye-view (BEV) perception has shown promising potential
in autonomous driving. Recent works mainly focus on improving efficiency or
accuracy but neglect the challenges when facing environment changing, resulting
in severe degradation of transfer performance. For BEV perception, we figure
out the significant domain gaps existing in typical real-world cross-domain
scenarios and comprehensively solve the Domain Adaption (DA) problem for
multi-view 3D object detection. Since BEV perception approaches are complicated
and contain several components, the domain shift accumulation on multiple
geometric spaces (i.e., 2D, 3D Voxel, BEV) makes BEV DA even challenging. In
this paper, we propose a Multi-space Alignment Teacher-Student (MATS) framework
to ease the domain shift accumulation, which consists of a Depth-Aware Teacher
(DAT) and a Geometric-space Aligned Student (GAS) model. DAT tactfully combines
target lidar and reliable depth prediction to construct depth-aware
information, extracting target domain-specific knowledge in Voxel and BEV
feature spaces. It then transfers the sufficient domain knowledge of multiple
spaces to the student model. In order to jointly alleviate the domain shift,
GAS projects multi-geometric space features to a shared geometric embedding
space and decreases data distribution distance between two domains. To verify
the effectiveness of our method, we conduct BEV 3D object detection experiments
on three cross-domain scenarios and achieve state-of-the-art performance.
