Central Angle Optimization for 360-degree Holographic 3D Content
This work addresses the computational cost and quality issues in hologram generation for 3D content creators, but it is incremental as it focuses on optimizing a specific parameter within an existing framework.
The study tackled the problem of generating high-quality holographic 3D content by optimizing the central angle between camera viewpoints for depth map estimation, resulting in an experimentally determined optimal angle that improves hologram quality.
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.