OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912039395393536 |
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| author | Wei, Jiale Zheng, Junwei Liu, Ruiping Hu, Jie Zhang, Jiaming Stiefelhagen, Rainer |
| author_facet | Wei, Jiale Zheng, Junwei Liu, Ruiping Hu, Jie Zhang, Jiaming Stiefelhagen, Rainer |
| contents | In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimations, often face calibration and synchronization issues. To break the wall of the aforementioned challenges, in this work, we introduce OneBEV, a novel BEV semantic mapping approach using merely a single panoramic image as input, simplifying the mapping process and reducing computational complexities. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas, transforming front-view features into BEV features without leveraging traditional attention mechanisms. Apart from the efficient framework, we contribute two datasets, i.e., nuScenes-360 and DeepAccident-360, tailored for the OneBEV task. Experimental results showcase that OneBEV achieves state-of-the-art performance with 51.1% and 36.1% mIoU on nuScenes-360 and DeepAccident-360, respectively. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13912 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping Wei, Jiale Zheng, Junwei Liu, Ruiping Hu, Jie Zhang, Jiaming Stiefelhagen, Rainer Computer Vision and Pattern Recognition In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimations, often face calibration and synchronization issues. To break the wall of the aforementioned challenges, in this work, we introduce OneBEV, a novel BEV semantic mapping approach using merely a single panoramic image as input, simplifying the mapping process and reducing computational complexities. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas, transforming front-view features into BEV features without leveraging traditional attention mechanisms. Apart from the efficient framework, we contribute two datasets, i.e., nuScenes-360 and DeepAccident-360, tailored for the OneBEV task. Experimental results showcase that OneBEV achieves state-of-the-art performance with 51.1% and 36.1% mIoU on nuScenes-360 and DeepAccident-360, respectively. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems. |
| title | OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.13912 |