Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. The existing multi-camera algorithms primarily rely on monocular 2D pre-training. However, the monocular 2D pre-training overlooks the spatial and temporal correlations among the multi-camera system. To address this limitation, we propose the first multi-camera unified pre-training framework, called UniScene, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, we employ Occupancy as the general representation for the 3D scene, enabling the model to grasp geometric priors of the surrounding world through pre-training. A significant benefit of UniScene is its capability to utilize a considerable volume of unlabeled image-LiDAR pairs for pre-training purposes. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and surrounding semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, UniScene shows a significant improvement of about 2.0% in mAP and 2.0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion. By adopting our unified pre-training method, a 25% reduction in 3D training annotation costs can be achieved, offering significant practical value for the implementation of real-world autonomous driving. Codes are publicly available at https://github.com/chaytonmin/UniScene.

翻译：多相机三维感知已成为自动驾驶领域的一个突出研究方向，为基于激光雷达的方案提供了一种可行且经济高效的替代选择。现有的多相机算法主要依赖于单目二维预训练。然而，单目二维预训练忽视了多相机系统间的时空相关性。为解决这一局限，我们提出了首个多相机统一预训练框架，称为UniScene，其核心是先以三维场景重建作为基础阶段，随后在下游任务上对模型进行微调。具体而言，我们采用Occupancy作为三维场景的通用表示，使模型通过预训练掌握周围世界的几何先验知识。UniScene的一大显著优势在于，它能利用大量未标注的图像-激光雷达对进行预训练。所提出的多相机统一预训练框架在多相机三维目标检测和周围语义场景补全等关键任务中展现出令人瞩目的结果。与在nuScenes数据集上采用单目预训练方法相比，UniScene在多相机三维目标检测的mAP和NDS上分别提升了约2.0%和2.0%，并在周围语义场景补全的mIoU上提升了3%。通过采用我们的统一预训练方法，三维训练标注成本可降低25%，为现实世界自动驾驶的实现提供了重要的实用价值。代码已在https://github.com/chaytonmin/UniScene开源。