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.

在自动驾驶中，通过分析网络效应和延迟，本文提出了一种名为 FastOcc 的新方法，通过用轻量级的 2D BEV 卷积网络代替时间消耗较大的 3D 卷积网络，加快模型的推理速度，同时保持准确性，从而实现了优越的性能。