Researchers at the University of California, Los Angeles, have engineered a compact snapshot three-dimensional imaging system capable of projecting twenty-eight distinct axial layers in a single optical exposure. Led by Professor Aydogan Ozcan of the UCLA Samueli School of Engineering and the California NanoSystems Institute, the project introduces a hybrid digital-optical architecture that resolves persistent diffraction crosstalk in dense depth multiplexing. The work was published in Light: Science & Applications. Traditional holographic displays degrade in depth selectivity and image clarity when multiple focal planes are positioned in close proximity. The UCLA team circumvents this limitation by end-to-end optimizing a computational digital encoder alongside a passive multi-layer diffractive decoder using deep learning. The encoder, built upon a Fourier-based neural network, extracts multiscale spatial and frequency-domain features from a target image stack and integrates precise axial positioning data. It outputs a single phase pattern that concurrently represents all projected layers. During propagation, the structurally optimized diffractive surfaces physically program the light field, directing image content to designated depth planes while intrinsically suppressing inter-plane signal leakage. Numerical simulations verify the framework’s scalability, demonstrating high-fidelity multiplane projection with axial separations near a single wavelength and successful encoding of twenty-eight axial slices into one phase pattern. The study also maps essential design variables, including decoder thickness, output diffraction efficiency, spatial light modulator resolution, and axial encoding density, establishing practical engineering benchmarks for commercial hardware. Experimental validation reinforces these findings. Using a two-plane optical prototype with a single-layer physical decoder in the visible spectrum, the team recorded intensity distributions that closely aligned with simulation predictions. The hardware substantially outperformed unassisted free-space optical baselines, confirming the operational viability of the jointly optimized digital-optical pipeline for practical snapshot 3D projection. This architecture delivers a scalable, energy-efficient foundation for high-axial-resolution volumetric imaging. Target applications include near-eye augmented and virtual reality displays, holographic visualization interfaces, multi-depth volumetric microscopy, real-time three-dimensional rendering, and optical computing. Subsequent research will explore multispectral integration, multiperspective holography, and the deployment of physically fabricated multilayer passive decoders for commercial form factors. The investigation was conducted by Dr. Çağatay Işıl, Alexander Chen, Yuhang Li, F. Onuralp Ardic, Dr. Shiqi Chen, Che-Yung Shen, and Professor Aydogan Ozcan from the UCLA Department of Electrical and Computer Engineering and the California NanoSystems Institute.