FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D
  Bird's-Eye View and Perspective View
Jiawei Hou Xiaoyan Li Wenhao Guan Gang Zhang Di Feng Yuheng Du Xiangyang Xue Jian Pu
In autonomous driving, 3D occupancy prediction outputs voxel-wise status and
semantic labels for more comprehensive understandings of 3D scenes compared
with traditional perception tasks, such as 3D object detection and bird's-eye
view (BEV) semantic segmentation. Recent researchers have extensively explored
various aspects of this task, including view transformation techniques,
ground-truth label generation, and elaborate network design, aiming to achieve
superior performance. However, the inference speed, crucial for running on an
autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is
proposed. By carefully analyzing the network effect and latency from four
parts, including the input image resolution, image backbone, view
transformation, and occupancy prediction head, it is found that the occupancy
prediction head holds considerable potential for accelerating the model while
keeping its accuracy. Targeted at improving this component, the time-consuming
3D convolution network is replaced with a novel residual-like architecture,
where features are mainly digested by a lightweight 2D BEV convolution network
and compensated by integrating the 3D voxel features interpolated from the
original image features. Experiments on the Occ3D-nuScenes benchmark
demonstrate that our FastOcc achieves state-of-the-art results with a fast
inference speed.