AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset

• URL: http://arxiv.org/abs/2306.00612v3
• Date: Thu, 26 Oct 2023 15:20:31 GMT
• Title: AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
• Authors: Jiakang Yuan, Bo Zhang, Xiangchao Yan, Tao Chen, Botian Shi, Yikang Li, Yu Qiao
• Abstract summary: It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud 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. We achieve significant performance gains on a series of downstream perception benchmarks including nuScenes, and KITTI, under different baseline models.
• Score: 25.935496432142976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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