  Given the capability of mitigating the long-tail deficiencies and
intricate-shaped absence prevalent in 3D object detection, occupancy prediction
has become a pivotal component in autonomous driving systems. However, the
procession of three-dimensional voxel-level representations inevitably
introduces large overhead in both memory and computation, obstructing the
deployment of to-date occupancy prediction approaches. In contrast to the trend
of making the model larger and more complicated, we argue that a desirable
framework should be deployment-friendly to diverse chips while maintaining high
precision. To this end, we propose a plug-and-play paradigm, namely FlashOCC,
to consolidate rapid and memory-efficient occupancy prediction while
maintaining high precision. Particularly, our FlashOCC makes two improvements
based on the contemporary voxel-level occupancy prediction approaches. Firstly,
the features are kept in the BEV, enabling the employment of efficient 2D
convolutional layers for feature extraction. Secondly, a channel-to-height
transformation is introduced to lift the output logits from the BEV into the 3D
space. We apply the FlashOCC to diverse occupancy prediction baselines on the
challenging Occ3D-nuScenes benchmarks and conduct extensive experiments to
validate the effectiveness. The results substantiate the superiority of our
plug-and-play paradigm over previous state-of-the-art methods in terms of
precision, runtime efficiency, and memory costs, demonstrating its potential
for deployment. The code will be made available.
