  Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results,
but they often do not generalize well to target domains outside the source (or
training) data distribution. To reduce such domain gaps and thus to make 3DOD
models more generalizable, we introduce a novel unsupervised domain adaptation
(UDA) method, called CMDA, which (i) leverages visual semantic cues from an
image modality (i.e., camera images) as an effective semantic bridge to close
the domain gap in the cross-modal Bird's Eye View (BEV) representations.
Further, (ii) we also introduce a self-training-based learning strategy,
wherein a model is adversarially trained to generate domain-invariant features,
which disrupt the discrimination of whether a feature instance comes from a
source or an unseen target domain. Overall, our CMDA framework guides the 3DOD
model to generate highly informative and domain-adaptive features for novel
data distributions. In our extensive experiments with large-scale benchmarks,
such as nuScenes, Waymo, and KITTI, those mentioned above provide significant
performance gains for UDA tasks, achieving state-of-the-art performance.
