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.

本研究提出了一种在基于深度学习的深度图估计中找到最佳中心角度的方法，用于产生逼真的全息内容。我们通过分析不同中心角度的数值，比较估计的深度图和从RGB图像和估计的深度图重建的CGH（计算机生成全息图）之间的关系，实验性地展示和讨论中心角度与数字全息内容的质量之间的关系。

360度全息三维内容的中心角优化