Deep Learning-Based Optimization of Central Angle and Viewpoint Configuration for 360-Degree Holographic Content

We present a deep learning-based approach to optimize the central angle between adjacent camera viewpoints for the efficient generation of natural 360-degree holographic 3D content. High-quality 360-degree digital holograms require the acquisition of densely sampled RGB–depth map pairs, a process that traditionally requires significant computational costs. Our method introduces a novel pipeline that systematically evaluates the impact of varying central angles—defined as the angular separation between equidistant viewpoints in an object-centered coordinate system—on both depth map estimation and holographic 3D image reconstruction. By systematically applying this pipeline, we determine the optimal central angle that achieves an effective balance between image quality and computational efficiency. Experimental investigations demonstrate that our approach significantly reduces computational demands while maintaining superior fidelity of the reconstructed 3D holographic images. The relationship between central angle selection and the resulting quality of 360-degree digital holographic 3D content is thoroughly analyzed, providing practical guidelines for the creation of immersive holographic video experiences. This work establishes a quantitative standard for the geometric configuration of viewpoint sampling in object-centered environments and advances the practical realization of real-time, high-quality holographic 3D content.