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
this https URL

本论文提出了一种新的域自适应自训练流水线（ST3D），用于基于点云的 3D 物体检测的无监督域自适应。该流水线通过特定的设计使检测器能够与一致且高质量的伪标签进行训练，并避免过度配合伪标记数据中大量的易样本，实现了对所有评估数据集的领先性能，并甚至超过了 KITTI 3D 目标检测基准的全监督结果。