Physically Guided Generative Adversarial Network for Holographic 3D Content Generation From Multi-View Light Field

Realizing high-fidelity three-dimensional (3D) scene representation through holography presents a formidable challenge, primarily due to the unknown mechanism of the optimal hologram and huge computational load as well as memory usage. Herein, we propose a Physically Guided Generative Adversarial Network (PGGAN), which is the first generative model to transform the multi-view light field directly to holographic 3D content. PGGAN harmoniously fuses the fidelity of data-driven learning with the rigor of physical optics principles, ensuring a stable reconstruction quality across wide field of view, which is unreachable by current central-view-centric approaches. The proposed framework presents an innovative encoder-generator-discriminator, which is informed by a physical optics model. It benefits from the speed and adaptability of data-driven methods to facilitate rapid learning and effectively transfer to novel scenes, while its physics-based guidance ensures that the generated holograms adhere to holographic standards. A unique, differentiable physical model facilitates end-to-end training, which aligns the generative process with the "holographic space", thereby improving the quality of the reconstructed light fields. Employing an adaptive loss strategy, PGGAN dynamically adjusts the influence of physical guidance in the initial training stages, later optimizing for reconstruction accuracy. Empirical evaluations reveal PGGAN's exceptional ability to swiftly generate a detailed hologram in as little as 0.002 seconds, significantly eclipsing current state-of-the-art techniques in speed while maintaining superior angular reconstruction fidelity. These results demonstrate PGGAN's effectiveness in producing high-quality holograms rapidly from multi-view datasets, advancing real-time holographic rendering significantly.