SafeMap: Robust HD Map Construction from Incomplete Observations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915367968833536 |
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| author | Hao, Xiaoshuai Kong, Lingdong Yin, Rong Wang, Pengwei Zhang, Jing Diao, Yunfeng Zhao, Shu |
| author_facet | Hao, Xiaoshuai Kong, Lingdong Yin, Rong Wang, Pengwei Zhang, Jing Diao, Yunfeng Zhao, Shu |
| contents | Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to secure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird's-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate the end-to-end map reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SafeMap: Robust HD Map Construction from Incomplete Observations Hao, Xiaoshuai Kong, Lingdong Yin, Rong Wang, Pengwei Zhang, Jing Diao, Yunfeng Zhao, Shu Computer Vision and Pattern Recognition Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to secure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird's-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate the end-to-end map reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and reliability. |
| title | SafeMap: Robust HD Map Construction from Incomplete Observations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.00861 |