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Scene-Customized Learning for Multi-Depth 3D Phase-Only Hologram Generation
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DOI：10.1002/aisy.70457.png)
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Abstract
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Convolutional neural network (CNN)–based computer-generated holography (CGH) enables high-speed, high-quality holographic display, yet its performance depends critically on the statistical characteristics of training datasets, and systematic frameworks for evaluating the performance boundaries of CNN-based hologram encoders remain lacking. From a dataset design perspective, this study proposes GM-4K, a 4K RGB-D dataset constructed through scene-customized geometric modeling. Using random geometric primitives, procedural textures, and parameterized spatial sampling, GM-4K generates scenes with controllable low-, mid-, high-, and wide-frequency intensity distributions, while also allowing flexible depth-region sampling. Numerical and optical experiments using a U-Net++ encoder demonstrate that the intensity spectral distribution of training data significantly influences the reconstruction quality of multi-depth phase-only holograms and that models trained on wide- or mid-frequency datasets exhibit better overall generalization in complex 3D scenes. Based on this observation, a spectral test framework comprising low-, mid-, and high-frequency scenes is developed to evaluate hologram encoding models under different frequency conditions. The proposed dataset construction paradigm and spectral test framework provide controllable data support for multi-depth hologram generation and offer practical guidance for data design and model evaluation in learning-based CGH.
Keywords:
computer-generated holography
convolutional neural network
multi-depth display
scene-customized learning
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