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.

翻译：自动驾驶汽车中精确的三维目标检测与理解高度依赖激光雷达点云数据，这需要大量标注数据进行训练。本文提出一种创新的预训练方法——接地气点着色（GPC），通过训练模型对激光雷达点云进行着色来弥合数据与标签之间的鸿沟，赋予模型有价值的语义线索。为解决颜色变化和选择偏差带来的挑战，我们将颜色作为"上下文"信息，在着色过程中提供真实颜色作为提示。在KITTI和Waymo数据集上的实验结果表明，GPC具有显著的有效性。即使在标注数据有限的情况下，GPC也能显著提升微调性能；值得注意的是，仅使用KITTI数据集20%的数据，GPC的性能就超越了使用完整数据集从头训练的模型。总之，我们为三维目标检测的预训练提供了全新视角，使预训练目标与模型预期功能相统一，最终提升了自动驾驶汽车三维目标检测的精度与效率。