  LiDAR-based 3D object detection is an indispensable task in advanced
autonomous driving systems. Though impressive detection results have been
achieved by superior 3D detectors, they suffer from significant performance
degeneration when facing unseen domains, such as different LiDAR
configurations, different cities, and weather conditions. The mainstream
approaches tend to solve these challenges by leveraging unsupervised domain
adaptation (UDA) techniques. However, these UDA solutions just yield
unsatisfactory 3D detection results when there is a severe domain shift, e.g.,
from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel
Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D),
where only a few labeled target data is available, yet can significantly
improve the adaptation performance. In particular, our SSDA3D includes an
Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the
first stage, an Inter-domain Point-CutMix module is presented to efficiently
align the point cloud distribution across domains. The Point-CutMix generates
mixed samples of an intermediate domain, thus encouraging to learn
domain-invariant knowledge. Then, in the second stage, we further enhance the
model for better generalization on the unlabeled target set. This is achieved
by exploring Intra-domain Point-MixUp in semi-supervised learning, which
essentially regularizes the pseudo label distribution. Experiments from Waymo
to nuScenes show that, with only 10% labeled target data, our SSDA3D can
surpass the fully-supervised oracle model with 100% target label. Our code is
available at https://github.com/yinjunbo/SSDA3D.
