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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2412.16938 |
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| _version_ | 1866916538495270912 |
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| author | Ji, Yishen Li, Zhiqi Lu, Tong |
| author_facet | Ji, Yishen Li, Zhiqi Lu, Tong |
| contents | Track Mapless demands models to process multi-view images and Standard-Definition (SD) maps, outputting lane and traffic element perceptions along with their topological relationships. We propose a novel architecture that integrates SD map priors to improve lane line and area detection performance. Inspired by TopoMLP, our model employs a two-stage structure: perception and reasoning. The downstream topology head uses the output from the upstream detection head, meaning accuracy improvements in detection significantly boost downstream performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ImagineMap: Enhanced HD Map Construction with SD Maps Ji, Yishen Li, Zhiqi Lu, Tong Computer Vision and Pattern Recognition Track Mapless demands models to process multi-view images and Standard-Definition (SD) maps, outputting lane and traffic element perceptions along with their topological relationships. We propose a novel architecture that integrates SD map priors to improve lane line and area detection performance. Inspired by TopoMLP, our model employs a two-stage structure: perception and reasoning. The downstream topology head uses the output from the upstream detection head, meaning accuracy improvements in detection significantly boost downstream performance. |
| title | ImagineMap: Enhanced HD Map Construction with SD Maps |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.16938 |