Neural radiance fields-based holography [Invited]

• URL: http://arxiv.org/abs/2403.01137v2
• Date: Thu, 9 May 2024 23:59:40 GMT
• Title: Neural radiance fields-based holography [Invited]
• Authors: Minsung Kang, Fan Wang, Kai Kumano, Tomoyoshi Ito, Tomoyoshi Shimobaba,
• Abstract summary: This study presents a novel approach for generating holograms based on the neural radiance fields (NeRF) technique. NeRF is a state-of-the-art technique for 3D light-field reconstruction from 2D images based on volume rendering. We constructed a rendering pipeline directly from a 3D light field generated from 2D images by NeRF for hologram generation using deep neural networks.
• Score: 7.6563606969349856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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