  This paper explores the potential of curriculum learning in LiDAR-based 3D
object detection by proposing a curricular object manipulation (COM) framework.
The framework embeds the curricular training strategy into both the loss design
and the augmentation process. For the loss design, we propose the COMLoss to
dynamically predict object-level difficulties and emphasize objects of
different difficulties based on training stages. On top of the widely-used
augmentation technique called GT-Aug in LiDAR detection tasks, we propose a
novel COMAug strategy which first clusters objects in ground-truth database
based on well-designed heuristics. Group-level difficulties rather than
individual ones are then predicted and updated during training for stable
results. Model performance and generalization capabilities can be improved by
sampling and augmenting progressively more difficult objects into the training
samples. Extensive experiments and ablation studies reveal the superior and
generality of the proposed framework. The code is available at
https://github.com/ZZY816/COM.
