PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908288017235968 |
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| author | Wang, Changhao Zhang, Guanwen Cheng, Zhengyun Zhou, Wei |
| author_facet | Wang, Changhao Zhang, Guanwen Cheng, Zhengyun Zhou, Wei |
| contents | Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this paper, we introduce visual prompt learning for predicting depth across different environments within a unified model, and present a self-supervised learning framework called PromptMono. It employs a set of learnable parameters as visual prompts to capture domain-specific knowledge. To integrate prompting information into image representations, a novel gated cross prompting attention (GCPA) module is proposed, which enhances the depth estimation in diverse conditions. We evaluate the proposed PromptMono on the Oxford Robotcar dataset and the nuScenes dataset. Experimental results demonstrate the superior performance of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments Wang, Changhao Zhang, Guanwen Cheng, Zhengyun Zhou, Wei Computer Vision and Pattern Recognition Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this paper, we introduce visual prompt learning for predicting depth across different environments within a unified model, and present a self-supervised learning framework called PromptMono. It employs a set of learnable parameters as visual prompts to capture domain-specific knowledge. To integrate prompting information into image representations, a novel gated cross prompting attention (GCPA) module is proposed, which enhances the depth estimation in diverse conditions. We evaluate the proposed PromptMono on the Oxford Robotcar dataset and the nuScenes dataset. Experimental results demonstrate the superior performance of the proposed method. |
| title | PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.13796 |