  In this paper, we present a self-training method, named ST3D++, with a
holistic pseudo label denoising pipeline for unsupervised domain adaptation on
3D object detection. ST3D++ aims at reducing noise in pseudo label generation
as well as alleviating the negative impacts of noisy pseudo labels on model
training. First, ST3D++ pre-trains the 3D object detector on the labeled source
domain with random object scaling (ROS) which is designed to reduce target
domain pseudo label noise arising from object scale bias of the source domain.
Then, the detector is progressively improved through alternating between
generating pseudo labels and training the object detector with pseudo-labeled
target domain data. Here, we equip the pseudo label generation process with a
hybrid quality-aware triplet memory to improve the quality and stability of
generated pseudo labels. Meanwhile, in the model training stage, we propose a
source data assisted training strategy and a curriculum data augmentation
policy to effectively rectify noisy gradient directions and avoid model
over-fitting to noisy pseudo labeled data. These specific designs enable the
detector to be trained on meticulously refined pseudo labeled target data with
denoised training signals, and thus effectively facilitate adapting an object
detector to a target domain without requiring annotations. Finally, our method
is assessed on four 3D benchmark datasets (i.e., Waymo, KITTI, Lyft, and
nuScenes) for three common categories (i.e., car, pedestrian and bicycle).
ST3D++ achieves state-of-the-art performance on all evaluated settings,
outperforming the corresponding baseline by a large margin (e.g., 9.6% $\sim$
38.16% on Waymo $\rightarrow$ KITTI in terms of AP$_{\text{3D}}$), and even
surpasses the fully supervised oracle results on the KITTI 3D object detection
benchmark with target prior. Code will be available.
