  Occupancy prediction plays a pivotal role in autonomous driving (AD) due to
the fine-grained geometric perception and general object recognition
capabilities. However, existing methods often incur high computational costs,
which contradicts the real-time demands of AD. To this end, we first evaluate
the speed and memory usage of most public available methods, aiming to redirect
the focus from solely prioritizing accuracy to also considering efficiency. We
then identify a core challenge in achieving both fast and accurate performance:
\textbf{the strong coupling between geometry and semantic}. To address this
issue, 1) we propose a Geometric-Semantic Dual-Branch Network (GSDBN) with a
hybrid BEV-Voxel representation. In the BEV branch, a BEV-level temporal fusion
module and a U-Net encoder is introduced to extract dense semantic features. In
the voxel branch, a large-kernel re-parameterized 3D convolution is proposed to
refine sparse 3D geometry and reduce computation. Moreover, we propose a novel
BEV-Voxel lifting module that projects BEV features into voxel space for
feature fusion of the two branches. In addition to the network design, 2) we
also propose a Geometric-Semantic Decoupled Learning (GSDL) strategy. This
strategy initially learns semantics with accurate geometry using ground-truth
depth, and then gradually mixes predicted depth to adapt the model to the
predicted geometry. Extensive experiments on the widely-used Occ3D-nuScenes
benchmark demonstrate the superiority of our method, which achieves a 39.4 mIoU
with 20.0 FPS. This result is $\sim 3 \times$ faster and +1.9 mIoU higher
compared to FB-OCC, the winner of CVPR2023 3D Occupancy Prediction Challenge.
Our code will be made open-source.
