Neural radiance fields-based holography [Invited]
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913345619099648 |
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| author | Kang, Minsung Wang, Fan Kumano, Kai Ito, Tomoyoshi Shimobaba, Tomoyoshi |
| author_facet | Kang, Minsung Wang, Fan Kumano, Kai Ito, Tomoyoshi Shimobaba, Tomoyoshi |
| contents | This study presents a novel approach for generating holograms based on the neural radiance fields (NeRF) technique. Generating three-dimensional (3D) data is difficult in hologram computation. NeRF is a state-of-the-art technique for 3D light-field reconstruction from 2D images based on volume rendering. The NeRF can rapidly predict new-view images that do not include a training dataset. In this study, we constructed a rendering pipeline directly from a 3D light field generated from 2D images by NeRF for hologram generation using deep neural networks within a reasonable time. The pipeline comprises three main components: the NeRF, a depth predictor, and a hologram generator, all constructed using deep neural networks. The pipeline does not include any physical calculations. The predicted holograms of a 3D scene viewed from any direction were computed using the proposed pipeline. The simulation and experimental results are presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural radiance fields-based holography [Invited] Kang, Minsung Wang, Fan Kumano, Kai Ito, Tomoyoshi Shimobaba, Tomoyoshi Computer Vision and Pattern Recognition Graphics Image and Video Processing This study presents a novel approach for generating holograms based on the neural radiance fields (NeRF) technique. Generating three-dimensional (3D) data is difficult in hologram computation. NeRF is a state-of-the-art technique for 3D light-field reconstruction from 2D images based on volume rendering. The NeRF can rapidly predict new-view images that do not include a training dataset. In this study, we constructed a rendering pipeline directly from a 3D light field generated from 2D images by NeRF for hologram generation using deep neural networks within a reasonable time. The pipeline comprises three main components: the NeRF, a depth predictor, and a hologram generator, all constructed using deep neural networks. The pipeline does not include any physical calculations. The predicted holograms of a 3D scene viewed from any direction were computed using the proposed pipeline. The simulation and experimental results are presented. |
| title | Neural radiance fields-based holography [Invited] |
| topic | Computer Vision and Pattern Recognition Graphics Image and Video Processing |
| url | https://arxiv.org/abs/2403.01137 |