  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.
