  Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn
extensive attention due to its low cost and high efficiency. Although new
algorithms for camera-only 3D object detection have been continuously proposed,
most of them may risk drastic performance degradation when the domain of input
images differs from that of training. In this paper, we first analyze the
causes of the domain gap for the MV3D-Det task. Based on the covariate shift
assumption, we find that the gap mainly attributes to the feature distribution
of BEV, which is determined by the quality of both depth estimation and 2D
image's feature representation. To acquire a robust depth prediction, we
propose to decouple the depth estimation from the intrinsic parameters of the
camera (i.e. the focal length) through converting the prediction of metric
depth to that of scale-invariant depth and perform dynamic perspective
augmentation to increase the diversity of the extrinsic parameters (i.e. the
camera poses) by utilizing homography. Moreover, we modify the focal length
values to create multiple pseudo-domains and construct an adversarial training
loss to encourage the feature representation to be more domain-agnostic.
Without bells and whistles, our approach, namely DG-BEV, successfully
alleviates the performance drop on the unseen target domain without impairing
the accuracy of the source domain. Extensive experiments on various public
datasets, including Waymo, nuScenes, and Lyft, demonstrate the generalization
and effectiveness of our approach. To the best of our knowledge, this is the
first systematic study to explore a domain generalization method for MV3D-Det.
