  In this paper, we propose a novel, convolutional neural network model to
extract highly precise depth maps from missing viewpoints, especially well
applicable to generate holographic 3D contents. The depth map is an essential
element for phase extraction which is required for synthesis of
computer-generated hologram (CGH). The proposed model called the HDD Net uses
MSE for the better performance of depth map estimation as loss function, and
utilizes the bilinear interpolation in up sampling layer with the Relu as
activation function. We design and prepare a total of 8,192 multi-view images,
each resolution of 640 by 360 for the deep learning study. The proposed model
estimates depth maps through extracting features, up sampling. For quantitative
assessment, we compare the estimated depth maps with the ground truths by using
the PSNR, ACC, and RMSE. We also compare the CGH patterns made from estimated
depth maps with ones made from ground truths. Furthermore, we demonstrate the
experimental results to test the quality of estimated depth maps through
directly reconstructing holographic 3D image scenes from the CGHs.
