Neural radiance fields-based holography [Invited]
Minsung Kang Fan Wang Kai Kumano Tomoyoshi Ito Tomoyoshi Shimobaba
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.