  3D object detectors based only on LiDAR point clouds hold the
state-of-the-art on modern street-view benchmarks. However, LiDAR-based
detectors poorly generalize across domains due to domain shift. In the case of
LiDAR, in fact, domain shift is not only due to changes in the environment and
in the object appearances, as for visual data from RGB cameras, but is also
related to the geometry of the point clouds (e.g., point density variations).
This paper proposes SF-UDA$^{3D}$, the first Source-Free Unsupervised Domain
Adaptation (SF-UDA) framework to domain-adapt the state-of-the-art PointRCNN 3D
detector to target domains for which we have no annotations (unsupervised),
neither we hold images nor annotations of the source domain (source-free).
SF-UDA$^{3D}$ is novel on both aspects. Our approach is based on
pseudo-annotations, reversible scale-transformations and motion coherency.
SF-UDA$^{3D}$ outperforms both previous domain adaptation techniques based on
features alignment and state-of-the-art 3D object detection methods which
additionally use few-shot target annotations or target annotation statistics.
This is demonstrated by extensive experiments on two large-scale datasets,
i.e., KITTI and nuScenes.
