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.

通过引入一种名为 CMDA 的新型无监督领域适应方法，利用图像模态（即相机图像）中的视觉语义线索作为一种有效的语义桥梁来缩小跨模态鸟瞰图表示的领域差距，并采用对抗性自我训练策略，使模型生成具有领域不变性的特征，从而为新的数据分布提供高度信息化和领域自适应的 3D 目标检测模型。在包括 nuScenes、Waymo 和 KITTI 在内的大规模基准测试中，以上所述方法为无监督领域适应任务带来了显著的性能提升，并达到了最先进的性能水平。