  3D object detection aims to recover the 3D information of concerning objects
and serves as the fundamental task of autonomous driving perception. Its
performance greatly depends on the scale of labeled training data, yet it is
costly to obtain high-quality annotations for point cloud data. While
conventional methods focus on generating pseudo-labels for unlabeled samples as
supplements for training, the structural nature of 3D point cloud data
facilitates the composition of objects and backgrounds to synthesize realistic
scenes. Motivated by this, we propose a hardness-aware scene synthesis (HASS)
method to generate adaptive synthetic scenes to improve the generalization of
the detection models. We obtain pseudo-labels for unlabeled objects and
generate diverse scenes with different compositions of objects and backgrounds.
As the scene synthesis is sensitive to the quality of pseudo-labels, we further
propose a hardness-aware strategy to reduce the effect of low-quality
pseudo-labels and maintain a dynamic pseudo-database to ensure the diversity
and quality of synthetic scenes. Extensive experimental results on the widely
used KITTI and Waymo datasets demonstrate the superiority of the proposed HASS
method, which outperforms existing semi-supervised learning methods on 3D
object detection. Code: https://github.com/wzzheng/HASS.
