Hybridnet for depth estimation and semantic segmentation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917586273304576 |
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| author | Sánchez-Escobedo, Dalila Lin, Xiao Casas, Josep R. Pardàs, Montse |
| author_facet | Sánchez-Escobedo, Dalila Lin, Xiao Casas, Josep R. Pardàs, Montse |
| contents | Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation,depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybridnet for depth estimation and semantic segmentation Sánchez-Escobedo, Dalila Lin, Xiao Casas, Josep R. Pardàs, Montse Computer Vision and Pattern Recognition Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation,depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on. |
| title | Hybridnet for depth estimation and semantic segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.06539 |