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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/2410.08567 |
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| _version_ | 1866909796371791872 |
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| author | Sun, Tianyu Hu, Dingchang Dai, Yixiang Wang, Guijin |
| author_facet | Sun, Tianyu Hu, Dingchang Dai, Yixiang Wang, Guijin |
| contents | Transparent and reflective objects, which are common in our everyday lives, present a significant challenge to 3D imaging techniques due to their unique visual and optical properties. Faced with these types of objects, RGB-D cameras fail to capture the real depth value with their accurate spatial information. To address this issue, we propose DITR, a diffusion-based Depth Inpainting framework specifically designed for Transparent and Reflective objects. This network consists of two stages, including a Region Proposal stage and a Depth Inpainting stage. DITR dynamically analyzes the optical and geometric depth loss and inpaints them automatically. Furthermore, comprehensive experimental results demonstrate that DITR is highly effective in depth inpainting tasks of transparent and reflective objects with robust adaptability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion-Based Depth Inpainting for Transparent and Reflective Objects Sun, Tianyu Hu, Dingchang Dai, Yixiang Wang, Guijin Computer Vision and Pattern Recognition Transparent and reflective objects, which are common in our everyday lives, present a significant challenge to 3D imaging techniques due to their unique visual and optical properties. Faced with these types of objects, RGB-D cameras fail to capture the real depth value with their accurate spatial information. To address this issue, we propose DITR, a diffusion-based Depth Inpainting framework specifically designed for Transparent and Reflective objects. This network consists of two stages, including a Region Proposal stage and a Depth Inpainting stage. DITR dynamically analyzes the optical and geometric depth loss and inpaints them automatically. Furthermore, comprehensive experimental results demonstrate that DITR is highly effective in depth inpainting tasks of transparent and reflective objects with robust adaptability. |
| title | Diffusion-Based Depth Inpainting for Transparent and Reflective Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.08567 |