Central Angle Optimization for 360-degree Holographic 3D Content
Hakdong Kim Minsung Yoon Cheongwon Kim
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.