  We present a new domain adaptive self-training pipeline, named ST3D, for
unsupervised domain adaptation on 3D object detection from point clouds. First,
we pre-train the 3D detector on the source domain with our proposed random
object scaling strategy for mitigating the negative effects of source domain
bias. Then, the detector is iteratively improved on the target domain by
alternatively conducting two steps, which are the pseudo label updating with
the developed quality-aware triplet memory bank and the model training with
curriculum data augmentation. These specific designs for 3D object detection
enable the detector to be trained with consistent and high-quality pseudo
labels and to avoid overfitting to the large number of easy examples in pseudo
labeled data. Our ST3D achieves state-of-the-art performance on all evaluated
datasets and even surpasses fully supervised results on KITTI 3D object
detection benchmark. Code will be available at
https://github.com/CVMI-Lab/ST3D.
