Far3D: Expanding the Horizon for Surround-view 3D Object Detection
| Autor: | Jiang, Xiaohui, Li, Shuailin, Liu, Yingfei, Wang, Shihao, Jia, Fan, Wang, Tiancai, Han, Lijin, Zhang, Xiangyu |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception range while leaving long-range detection less explored. Expanding existing methods directly to cover long distances poses challenges such as heavy computation costs and unstable convergence. To address these limitations, this paper proposes a novel sparse query-based framework, dubbed Far3D. By utilizing high-quality 2D object priors, we generate 3D adaptive queries that complement the 3D global queries. To efficiently capture discriminative features across different views and scales for long-range objects, we introduce a perspective-aware aggregation module. Additionally, we propose a range-modulated 3D denoising approach to address query error propagation and mitigate convergence issues in long-range tasks. Significantly, Far3D demonstrates SoTA performance on the challenging Argoverse 2 dataset, covering a wide range of 150 meters, surpassing several LiDAR-based approaches. Meanwhile, Far3D exhibits superior performance compared to previous methods on the nuScenes dataset. The code is available at https://github.com/megvii-research/Far3D. Comment: Accepted by AAAI-2024 |
| Databáze: | arXiv |
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