  3D object detection using LiDAR point clouds is a fundamental task in the
fields of computer vision, robotics, and autonomous driving. However, existing
3D detectors heavily rely on annotated datasets, which are both time-consuming
and prone to errors during the process of labeling 3D bounding boxes. In this
paper, we propose a Scene Completion Pre-training (SCP) method to enhance the
performance of 3D object detectors with less labeled data. SCP offers three key
advantages: (1) Improved initialization of the point cloud model. By completing
the scene point clouds, SCP effectively captures the spatial and semantic
relationships among objects within urban environments. (2) Elimination of the
need for additional datasets. SCP serves as a valuable auxiliary network that
does not impose any additional efforts or data requirements on the 3D
detectors. (3) Reduction of the amount of labeled data for detection. With the
help of SCP, the existing state-of-the-art 3D detectors can achieve comparable
performance while only relying on 20% labeled data.
