  Accurate 3D object detection and understanding for self-driving cars heavily
relies on LiDAR point clouds, necessitating large amounts of labeled data to
train. In this work, we introduce an innovative pre-training approach, Grounded
Point Colorization (GPC), to bridge the gap between data and labels by teaching
the model to colorize LiDAR point clouds, equipping it with valuable semantic
cues. To tackle challenges arising from color variations and selection bias, we
incorporate color as "context" by providing ground-truth colors as hints during
colorization. Experimental results on the KITTI and Waymo datasets demonstrate
GPC's remarkable effectiveness. Even with limited labeled data, GPC
significantly improves fine-tuning performance; notably, on just 20% of the
KITTI dataset, GPC outperforms training from scratch with the entire dataset.
In sum, we introduce a fresh perspective on pre-training for 3D object
detection, aligning the objective with the model's intended role and ultimately
advancing the accuracy and efficiency of 3D object detection for autonomous
vehicles.
