Neural radiance fields-based holography [Invited]
This addresses the problem of efficient hologram generation for 3D visualization, but it appears incremental as it adapts existing NeRF methods to a specific application.
The study tackled the difficulty of generating 3D data for hologram computation by proposing a pipeline based on neural radiance fields (NeRF) to predict holograms from 2D images without physical calculations, with results validated through simulation and experiments.
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.