83‐4: Invited Paper: Filter‐Free 3D HoloNet with Hardware‐Aware Calibration

Computational holography faces challenges in balancing speed and quality, especially for 3D content. This work presents 3D‐HoloNet, a deep learning framework that generate phase‐only holograms from RGB‐D scenes in real time. The method integrates a learned wave propagation model calibrated to physical displays and a phase regularization strategy, enabling robust performance under hardware imperfections. Experiments show the system achieves 30 fps at full‐HD resolution (single‐color channel) on consumer GPUs while matching iterative methods in reconstruction quality across multiple focal planes. By eliminating iterative optimization and physical filtering, 3D‐HoloNet addresses the critical speed‐quality trade‐off in unfiltered holographic displays.