Stream and Query-guided Feature Aggregation for Efficient and Effective 3D Occupancy Prediction
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909926019825664 |
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| author | Moon, Seokha Baek, Janghyun Kim, Giseop Kim, Jinkyu Choi, Sunwook |
| author_facet | Moon, Seokha Baek, Janghyun Kim, Giseop Kim, Jinkyu Choi, Sunwook |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_22087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stream and Query-guided Feature Aggregation for Efficient and Effective 3D Occupancy Prediction Moon, Seokha Baek, Janghyun Kim, Giseop Kim, Jinkyu Choi, Sunwook Computer Vision and Pattern Recognition 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. |
| title | Stream and Query-guided Feature Aggregation for Efficient and Effective 3D Occupancy Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.22087 |