Let Occ Flow: Self-Supervised 3D Occupancy Flow Prediction
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909341689315328 |
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| author | Liu, Yili Mou, Linzhan Yu, Xuan Han, Chenrui Mao, Sitong Xiong, Rong Wang, Yue |
| author_facet | Liu, Yili Mou, Linzhan Yu, Xuan Han, Chenrui Mao, Sitong Xiong, Rong Wang, Yue |
| contents | Accurate perception of the dynamic environment is a fundamental task for autonomous driving and robot systems. This paper introduces Let Occ Flow, the first self-supervised work for joint 3D occupancy and occupancy flow prediction using only camera inputs, eliminating the need for 3D annotations. Utilizing TPV for unified scene representation and deformable attention layers for feature aggregation, our approach incorporates a novel attention-based temporal fusion module to capture dynamic object dependencies, followed by a 3D refine module for fine-gained volumetric representation. Besides, our method extends differentiable rendering to 3D volumetric flow fields, leveraging zero-shot 2D segmentation and optical flow cues for dynamic decomposition and motion optimization. Extensive experiments on nuScenes and KITTI datasets demonstrate the competitive performance of our approach over prior state-of-the-art methods. Our project page is available at https://eliliu2233.github.io/letoccflow/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Let Occ Flow: Self-Supervised 3D Occupancy Flow Prediction Liu, Yili Mou, Linzhan Yu, Xuan Han, Chenrui Mao, Sitong Xiong, Rong Wang, Yue Computer Vision and Pattern Recognition Accurate perception of the dynamic environment is a fundamental task for autonomous driving and robot systems. This paper introduces Let Occ Flow, the first self-supervised work for joint 3D occupancy and occupancy flow prediction using only camera inputs, eliminating the need for 3D annotations. Utilizing TPV for unified scene representation and deformable attention layers for feature aggregation, our approach incorporates a novel attention-based temporal fusion module to capture dynamic object dependencies, followed by a 3D refine module for fine-gained volumetric representation. Besides, our method extends differentiable rendering to 3D volumetric flow fields, leveraging zero-shot 2D segmentation and optical flow cues for dynamic decomposition and motion optimization. Extensive experiments on nuScenes and KITTI datasets demonstrate the competitive performance of our approach over prior state-of-the-art methods. Our project page is available at https://eliliu2233.github.io/letoccflow/ |
| title | Let Occ Flow: Self-Supervised 3D Occupancy Flow Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.07587 |