A Deep Learning Framework for Multi-Plane Computer-Generated Holography

Multi-plane computer-generated holography is a key technology for enabling volumetric and near-eye displays. However, its widespread adoption remains constrained by the high computational cost of phase optimization and the persistent issue of axial crosstalk between depth planes. In this work, we propose a physics-informed deep learning framework that directly generates holograms for 3D multi-plane displays. Our approach implements a learnable mapping from spatial distributions to depth-dependent reconstructions and incorporates a trainable Fourier transform layer, enabling end-to-end optimization entirely in the physical domain (i.e., from the hologram plane to the multi-plane reconstruction). As a result, hologram generation time is decreased significantly, while effectively suppressing crosstalk across axial planes. Experimental validation demonstrates that the obtained phase hologram successfully reconstructs sparse multi-plane structured patterns with low visible crosstalk. These results highlight the potential of deep learning to advance practical applications in dynamic 3D display and holographic optical tweezer technologies.