Stream and Query-guided Feature Aggregation for Efficient and Effective 3D Occupancy Prediction

Engineering notes

Experiments on the widely used Occ3D-nuScenes and SurroundOcc datasets demonstrate that DuOcc achieves state-of-the-art performance in real-time settings, while reducing memory usage by over 40% compared to prior methods.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

3D occupancy prediction has become a key perception task in autonomous driving, as it enables comprehensive scene understanding. Recent methods enhance this understanding by incorporating spatiotemporal information through multi-frame fusion, but they suffer from a trade-off: dense voxel-based representations provide high accuracy at significant computational cost, whereas sparse representations improve efficiency but lose spatial detail. To mitigate this trade-off, we introduce DuOcc, which employs a dual aggregation strategy that retains dense voxel representations to preserve spatial fidelity while maintaining high efficiency. DuOcc consists of two key components: (i) Stream-based Voxel Aggregation, which recurrently accumulates voxel features over time and refines them to suppress warping-induced distortions, preserving a clear separation between occupied and free space. (ii) Query-guided Aggregation, which complements the limitations of voxel accumulation by selectively injecting instance-level query features into the voxel regions occupied by dynamic objects. Experiments on the widely used Occ3D-nuScenes and SurroundOcc datasets demonstrate that DuOcc achieves state-of-the-art performance in real-time settings, while reducing memory usage by over 40% compared to prior methods.

Links and sources

• Official / arXiv page
• PDF from original source

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