Full 3D Hologram

Full 3D hologram generation aims to create realistic, viewable three-dimensional images computationally, overcoming limitations in computational cost and image quality. Current research focuses on improving efficiency through neural network architectures, including quantized networks and rotationally equivariant convolutional neural networks, and employing techniques like self-supervised learning and physics-driven generative adversarial networks to reduce reliance on large labeled datasets. These advancements are driving progress in applications such as augmented and virtual reality, holographic displays, and improved microscopic imaging, with a particular emphasis on achieving high-fidelity reconstructions in real-time on portable devices.