In this paper, we propose a novel model to extract highly precise depth maps from missing viewpoints, especially for generating holographic 3D content. These depth maps are essential elements for phase extraction, which is required for the synthesis of computer-generated holograms (CGHs). The proposed model, called the holographic dense depth, estimates depth maps through feature extraction, combining, up-sampling. We designed and prepared a total of 8,192 multi-view images with resolutions of 640 × 360. We evaluated our model by comparing the estimated depth maps with their ground truths using peak signal-to-noise ratio, accuracy, and root mean squared error as performance measures. We further compared the CGH patterns created from estimated depth maps with those from ground truths and reconstructed the holographic 3D image scenes from their CGHs. Both quantitative and qualitative results demonstrate the effectiveness of the proposed approach.

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In this paper, we propose a novel model to extract highly precise depth maps from missing viewpoints, especially for generating holographic 3D content. These depth maps are essential elements for phase extraction, which is required for the synthesis of computer-generated holograms (CGHs). The proposed model, called the holographic dense depth, estimates depth maps through feature extraction, combining up-sampling. We designed and prepared a total of 9832 multi-view images with resolutions of 640 × 360. We evaluated our model by comparing the estimated depth maps with their ground truths using various metrics. We further compared the CGH patterns created from estimated depth maps with those from ground truths and reconstructed the holographic 3D image scenes from their CGHs. Both quantitative and qualitative results demonstrate the effectiveness of the proposed method.