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

翻译：为了观察全息三维内容，在使用快速傅里叶变换（FFT）算法生成计算机生成全息图（CGH）时，通常需要获取每幅场景的RGB彩色图像和深度图。然而，在实际应用中，这种RGB彩色图像与深度图的配对格式并不总是完全可用。我们提出一种基于深度学习的方法，仅利用给定的RGB图像即可合成立体数字全息图，从而克服RGB彩色图像与深度图部分可用的环境限制。该方法仅以RGB图像作为输入，先估计其深度图，再顺序生成对应的CGH。通过实验证明，在仅提供RGB彩色数据的情况下，我们的模型生成的立体全息图比竞争模型更精确。