Real-Time 3D Occupancy Prediction via Geometric-Semantic Disentanglement

TL;DR

This paper introduces a novel real-time 3D occupancy prediction method that balances accuracy and efficiency through geometric-semantic disentanglement, achieving state-of-the-art results with faster inference.

Contribution

It proposes a dual-branch network with hybrid BEV-Voxel representation and a decoupled learning strategy to improve speed and accuracy in 3D occupancy prediction.

Findings

Achieves 39.4 mIoU at 20 FPS, outperforming previous methods.

Reduces computational costs with re-parameterized 3D convolution.

Demonstrates superior performance on the Occ3D-nuScenes benchmark.

Abstract

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.