OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914693825691648 |
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| author | Liu, Jian Zhang, Sipeng Kong, Chuixin Zhang, Wenyuan Wu, Yuhang Ding, Yikang Xu, Borun Ming, Ruibo Wei, Donglai Liu, Xianming |
| author_facet | Liu, Jian Zhang, Sipeng Kong, Chuixin Zhang, Wenyuan Wu, Yuhang Ding, Yikang Xu, Borun Ming, Ruibo Wei, Donglai Liu, Xianming |
| contents | This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction Liu, Jian Zhang, Sipeng Kong, Chuixin Zhang, Wenyuan Wu, Yuhang Ding, Yikang Xu, Borun Ming, Ruibo Wei, Donglai Liu, Xianming Computer Vision and Pattern Recognition This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge. |
| title | OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.18140 |