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license: apache-2.0 |
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--- |
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# FlowOcc3D (3D Occypancy Flow Dataset) |
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<img src="./FlowOcc3D.jpg" width="800"/> |
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Download [FlowOcc3D](https://huggingface.co/viewformer/ViewFormer-Occ/blob/main/occ_flow_sparse_ext.zip). Unzip it in `./data/nuscenes`. |
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Our FlowOcc3D assigns a flow vector to each foreground occupancy of [Occ3D](https://github.com/CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction). |
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Here we briefly introduce how to use FlowOcc3D. We store the flow and index of each foreground voxel in the `xxx.bin` file and `xxx_idx.bin` file. |
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```python |
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W, H, Z = 200, 200, 16 |
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sample_idx = results['sample_idx'] # nuScenes sample token |
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data_path = os.path.join('./data/nuscenes', 'occ_flow_sparse_ext', sample_idx) |
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occ_flow = np.ones((W*H*Z, 2)) * pad_value # pad_value could be zero |
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sparse_flow = np.fromfile(data_path + '.bin', dtype=np.float16).reshape(-1, 3)[:, :2] |
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sparse_idx = np.fromfile(data_path + '_idx.bin', dtype=np.int32).reshape(-1) |
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occ_flow[sparse_idx] = sparse_flow |
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occ_flow = occ_flow.reshape(W, H, Z, 2) |
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``` |
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## Citation |
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```bibtex |
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@article{li2024viewformer, |
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title={ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided Transformers}, |
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author={Jinke Li and Xiao He and Chonghua Zhou and Xiaoqiang Cheng and Yang Wen and Dan Zhang}, |
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journal={arXiv preprint arXiv:2405.04299}, |
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year={2024}, |
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} |
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``` |
