  Existing 3D object detection suffers from expensive annotation costs and poor
transferability to unknown data due to the domain gap, Unsupervised Domain
Adaptation (UDA) aims to generalize detection models trained in labeled source
domains to perform robustly on unexplored target domains, providing a promising
solution for cross-domain 3D object detection. Although Self-Training (ST)
based cross-domain 3D detection methods with the assistance of pseudo-labeling
techniques have achieved remarkable progress, they still face the issue of
low-quality pseudo-labels when there are significant domain disparities due to
the absence of a process for feature distribution alignment. While Adversarial
Learning (AL) based methods can effectively align the feature distributions of
the source and target domains, the inability to obtain labels in the target
domain forces the adoption of asymmetric optimization losses, resulting in a
challenging issue of source domain bias. To overcome these limitations, we
propose a novel unsupervised domain adaptation framework for 3D object
detection via collaborating ST and AL, dubbed as STAL3D, unleashing the
complementary advantages of pseudo labels and feature distribution alignment.
Additionally, a Background Suppression Adversarial Learning (BS-AL) module and
a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain
scenes, effectively alleviating the issues of the large proportion of
background interference and source domain size bias. Our STAL3D achieves
state-of-the-art performance on multiple cross-domain tasks and even surpasses
the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$
KITTI-rain.
