  Deep learning has achieved notable success in 3D object detection with the
advent of large-scale point cloud datasets. However, severe performance
degradation in the past trained classes, i.e., catastrophic forgetting, still
remains a critical issue for real-world deployment when the number of classes
is unknown or may vary. Moreover, existing 3D class-incremental detection
methods are developed for the single-domain scenario, which fail when
encountering domain shift caused by different datasets, varying environments,
etc. In this paper, we identify the unexplored yet valuable scenario, i.e.,
class-incremental learning under domain shift, and propose a novel 3D domain
adaptive class-incremental object detection framework, DA-CIL, in which we
design a novel dual-domain copy-paste augmentation method to construct multiple
augmented domains for diversifying training distributions, thereby facilitating
gradual domain adaptation. Then, multi-level consistency is explored to
facilitate dual-teacher knowledge distillation from different domains for
domain adaptive class-incremental learning. Extensive experiments on various
datasets demonstrate the effectiveness of the proposed method over baselines in
the domain adaptive class-incremental learning scenario.
