A Vision-Centric Approach for Static Map Element Annotation
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913236047101952 |
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| author | Zhang, Jiaxin Chen, Shiyuan Yin, Haoran Mei, Ruohong Liu, Xuan Yang, Cong Zhang, Qian Sui, Wei |
| author_facet | Zhang, Jiaxin Chen, Shiyuan Yin, Haoran Mei, Ruohong Liu, Xuan Yang, Cong Zhang, Qian Sui, Wei |
| contents | The recent development of online static map element (a.k.a. HD Map) construction algorithms has raised a vast demand for data with ground truth annotations. However, available public datasets currently cannot provide high-quality training data regarding consistency and accuracy. To this end, we present CAMA: a vision-centric approach for Consistent and Accurate Map Annotation. Without LiDAR inputs, our proposed framework can still generate high-quality 3D annotations of static map elements. Specifically, the annotation can achieve high reprojection accuracy across all surrounding cameras and is spatial-temporal consistent across the whole sequence. We apply our proposed framework to the popular nuScenes dataset to provide efficient and highly accurate annotations. Compared with the original nuScenes static map element, models trained with annotations from CAMA achieve lower reprojection errors (e.g., 4.73 vs. 8.03 pixels). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11754 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Vision-Centric Approach for Static Map Element Annotation Zhang, Jiaxin Chen, Shiyuan Yin, Haoran Mei, Ruohong Liu, Xuan Yang, Cong Zhang, Qian Sui, Wei Computer Vision and Pattern Recognition The recent development of online static map element (a.k.a. HD Map) construction algorithms has raised a vast demand for data with ground truth annotations. However, available public datasets currently cannot provide high-quality training data regarding consistency and accuracy. To this end, we present CAMA: a vision-centric approach for Consistent and Accurate Map Annotation. Without LiDAR inputs, our proposed framework can still generate high-quality 3D annotations of static map elements. Specifically, the annotation can achieve high reprojection accuracy across all surrounding cameras and is spatial-temporal consistent across the whole sequence. We apply our proposed framework to the popular nuScenes dataset to provide efficient and highly accurate annotations. Compared with the original nuScenes static map element, models trained with annotations from CAMA achieve lower reprojection errors (e.g., 4.73 vs. 8.03 pixels). |
| title | A Vision-Centric Approach for Static Map Element Annotation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.11754 |