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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2311.07432 |
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| _version_ | 1866909119577849856 |
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| author | Melicherčík, Martin Gajdošech, Lukáš Kocur, Viktor Madaras, Martin |
| author_facet | Melicherčík, Martin Gajdošech, Lukáš Kocur, Viktor Madaras, Martin |
| contents | This paper focuses on increasing the resolution of depth maps obtained from 3D cameras using structured light technology. Two deep learning models FDSR and DKN are modified to work with high-resolution data, and data pre-processing techniques are implemented for stable training. The models are trained on our custom dataset of 1200 3D scans. The resulting high-resolution depth maps are evaluated using qualitative and quantitative metrics. The approach for depth map upsampling offers benefits such as reducing the processing time of a pipeline by first downsampling a high-resolution depth map, performing various processing steps at the lower resolution and upsampling the resulting depth map or increasing the resolution of a point cloud captured in lower resolution by a cheaper device. The experiments demonstrate that the FDSR model excels in terms of faster processing time, making it a suitable choice for applications where speed is crucial. On the other hand, the DKN model provides results with higher precision, making it more suitable for applications that prioritize accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07432 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Supersampling of Data from Structured-light Scanner with Deep Learning Melicherčík, Martin Gajdošech, Lukáš Kocur, Viktor Madaras, Martin Computer Vision and Pattern Recognition I.4.9 This paper focuses on increasing the resolution of depth maps obtained from 3D cameras using structured light technology. Two deep learning models FDSR and DKN are modified to work with high-resolution data, and data pre-processing techniques are implemented for stable training. The models are trained on our custom dataset of 1200 3D scans. The resulting high-resolution depth maps are evaluated using qualitative and quantitative metrics. The approach for depth map upsampling offers benefits such as reducing the processing time of a pipeline by first downsampling a high-resolution depth map, performing various processing steps at the lower resolution and upsampling the resulting depth map or increasing the resolution of a point cloud captured in lower resolution by a cheaper device. The experiments demonstrate that the FDSR model excels in terms of faster processing time, making it a suitable choice for applications where speed is crucial. On the other hand, the DKN model provides results with higher precision, making it more suitable for applications that prioritize accuracy. |
| title | Supersampling of Data from Structured-light Scanner with Deep Learning |
| topic | Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2311.07432 |