结合多数据集的自监督预训练用于自动驾驶中的3D感知
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving
AI总结：
针对自动驾驶3D感知，本文提出结合异构未标记数据集的自监督预训练框架，搭配提示适配器域适应策略，显著提升下游任务性能，且性能随数据量增加而稳定提升。
摘要：
预训练模型在自然语言处理（NLP）和2D视觉领域利用大量数据取得的显著成果，启发我们探索大规模数据预训练在自动驾驶3D感知中的潜力。为此，本文提出利用来自异构数据集的海量未标记数据预训练3D感知模型。我们引入一种自监督预训练框架，可从零开始从未标记数据中学习有效的3D表示，结合基于提示适配器（prompt adapter）的域适应策略以减少数据集偏差。该方法显著提升了3D目标检测、鸟瞰图（BEV）分割、3D目标跟踪和占用预测等下游任务的模型性能，且随着训练数据量增加表现出稳定的性能提升，证明了其持续助力自动驾驶3D感知模型的潜力。我们将发布源代码以启发社区进一步研究。
英文摘要：
The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-training for 3D perception in autonomous driving. Toward this goal, this paper proposes to utilize massive unlabeled data from heterogeneous datasets to pre-train 3D perception models. We introduce a self-supervised pre-training framework that learns effective 3D representations from scratch on unlabeled data, combined with a prompt adapter based domain adaptation strategy to reduce dataset bias. The approach significantly improves model performance on downstream tasks such as 3D object detection, BEV segmentation, 3D object tracking, and occupancy prediction, and shows steady performance increase as the training data volume scales up, demonstrating the potential of continually benefit 3D perception models for autonomous driving. We will release the source code to inspire further investigations in the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
