UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914945967325184 |
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| author | Ni, Tao Zhan, Xin Luo, Tao Liu, Wenbin Shi, Zhan Chen, JunBo |
| author_facet | Ni, Tao Zhan, Xin Luo, Tao Liu, Wenbin Shi, Zhan Chen, JunBo |
| contents | Road segmentation is a critical task for autonomous driving systems, requiring accurate and robust methods to classify road surfaces from various environmental data. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps derived from images. The integration of multiple data sources in road segmentation presents both opportunities and challenges. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets that are necessary for training robust deep learning models. To address this, we have developed the [UdeerLID+] framework under a semi-supervised learning paradigm. Experiments results on KITTI datasets validate the superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06197 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised Ni, Tao Zhan, Xin Luo, Tao Liu, Wenbin Shi, Zhan Chen, JunBo Computer Vision and Pattern Recognition Road segmentation is a critical task for autonomous driving systems, requiring accurate and robust methods to classify road surfaces from various environmental data. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps derived from images. The integration of multiple data sources in road segmentation presents both opportunities and challenges. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets that are necessary for training robust deep learning models. To address this, we have developed the [UdeerLID+] framework under a semi-supervised learning paradigm. Experiments results on KITTI datasets validate the superior performance. |
| title | UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.06197 |