It is a long-term vision for Autonomous Driving (AD) community that the
perception models can learn from a large-scale point cloud dataset, to obtain
unified representations that can achieve promising results on different tasks
or benchmarks. Previous works mainly focus on the self-supervised pre-training
pipeline, meaning that they perform the pre-training and fine-tuning on the
same benchmark, which is difficult to attain the performance scalability and
cross-dataset application for the pre-training checkpoint. In this paper, for
the first time, we are committed to building a large-scale pre-training
point-cloud dataset with diverse data distribution, and meanwhile learning
generalizable representations from such a diverse pre-training dataset. We
formulate the point-cloud pre-training task as a semi-supervised problem, which
leverages the few-shot labeled and massive unlabeled point-cloud data to
generate the unified backbone representations that can be directly applied to
many baseline models and benchmarks, decoupling the AD-related pre-training
process and downstream fine-tuning task. During the period of backbone
pre-training, by enhancing the scene- and instance-level distribution diversity
and exploiting the backbone's ability to learn from unknown instances, we
achieve significant performance gains on a series of downstream perception
benchmarks including Waymo, nuScenes, and KITTI, under different baseline
models like PV-RCNN++, SECOND, CenterPoint.

通过构建大规模点云数据集，将点云预训练任务形式化为一种半监督问题，并从这样多样化的预训练数据中学习可推广的表示，从而在多个基准模型和基准测试中取得了显著的性能增益。