In this study, we propose a method to find an optimal central angle in deep learning-based depth map estimation used to produce realistic holographic content. The acquisition of RGB-depth map images as detailed as possible must be performed to generate holograms of high quality, despite the high computational cost. Therefore, we introduce a novel pipeline designed to analyze various values of central angles between adjacent camera viewpoints equidistant from the origin of an object-centered environment. Then we propose the optimal central angle to generate high-quality holographic content. The proposed pipeline comprises key steps such as comparing estimated depth maps and comparing reconstructed CGHs (Computer-Generated Holograms) from RGB images and estimated depth maps. We experimentally demonstrate and discuss the relationship between the central angle and the quality of digital holographic content.

翻译：本研究提出一种方法，用于优化基于深度学习深度图估计中的中心角，以生成逼真的全息内容。尽管计算成本较高，但为了生成高质量全息图，必须获取尽可能详细的RGB深度图图像。为此，我们引入了一种新颖的处理流程，旨在分析以物体为中心环境中、与原点等距的相邻相机视点间不同中心角数值。随后，我们提出了生成高质量全息内容的最优中心角。该流程包括关键步骤，如比较估计的深度图，以及从RGB图像和估计的深度图重建计算生成全息图（CGH）并进行比较。我们通过实验论证并讨论了中心角与数字全息内容质量之间的关系。