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In preparation for observing holographic 3D content, acquiring a set of RGBcolor and depth map images per scene is necessary to generatecomputer-generated holograms (CGHs) when using the fast Fourier transform (FFT)algorithm. However, in real-world situations, these paired formats of RGB colorand depth map images are not always fully available. We propose a deeplearning-based method to synthesize the volumetric digital holograms using onlythe given RGB image, so that we can overcome environments where RGB color anddepth map images are partially provided. The proposed method uses only theinput of RGB image to estimate its depth map and then generate its CGHsequentially. Through experiments, we demonstrate that the volumetric hologramgenerated through our proposed model is more accurate than that of competitivemodels, under the situation that only RGB color data can be provided.

A novel approach for holographic 3D content generation without depth map