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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.02008 |
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| _version_ | 1866909301922070528 |
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| author | Jia, Peijin Wen, Tuopu Luo, Ziang Yang, Mengmeng Jiang, Kun Lei, Zhiquan Tang, Xuewei Liu, Ziyuan Cui, Le Zhang, Bo Huang, Long Yang, Diange |
| author_facet | Jia, Peijin Wen, Tuopu Luo, Ziang Yang, Mengmeng Jiang, Kun Lei, Zhiquan Tang, Xuewei Liu, Ziyuan Cui, Le Zhang, Bo Huang, Long Yang, Diange |
| contents | Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model Jia, Peijin Wen, Tuopu Luo, Ziang Yang, Mengmeng Jiang, Kun Lei, Zhiquan Tang, Xuewei Liu, Ziyuan Cui, Le Zhang, Bo Huang, Long Yang, Diange Computer Vision and Pattern Recognition Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps. |
| title | DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.02008 |