  The past few years have witnessed the rapid development of vision-centric 3D
perception in autonomous driving. Although the 3D perception models share many
structural and conceptual similarities, there still exist gaps in their feature
representations, data formats, and objectives, posing challenges for unified
and efficient 3D perception framework design. In this paper, we present
UniVision, a simple and efficient framework that unifies two major tasks in
vision-centric 3D perception, \ie, occupancy prediction and object detection.
Specifically, we propose an explicit-implicit view transform module for
complementary 2D-3D feature transformation. We propose a local-global feature
extraction and fusion module for efficient and adaptive voxel and BEV feature
extraction, enhancement, and interaction. Further, we propose a joint
occupancy-detection data augmentation strategy and a progressive loss weight
adjustment strategy which enables the efficiency and stability of the
multi-task framework training. We conduct extensive experiments for different
perception tasks on four public benchmarks, including nuScenes LiDAR
segmentation, nuScenes detection, OpenOccupancy, and Occ3D. UniVision achieves
state-of-the-art results with +1.5 mIoU, +1.8 NDS, +1.5 mIoU, and +1.8 mIoU
gains on each benchmark, respectively. We believe that the UniVision framework
can serve as a high-performance baseline for the unified vision-centric 3D
perception task. The code will be available at
\url{https://github.com/Cc-Hy/UniVision}.
