A novel approach for holographic 3D content generation without depth map
Hakdong Kim Minkyu Jee Yurim Lee Kyudam Choi MinSung Yoon Cheongwon Kim
In preparation for observing holographic 3D content, acquiring a set of RGB
color and depth map images per scene is necessary to generate
computer-generated holograms (CGHs) when using the fast Fourier transform (FFT)
algorithm. However, in real-world situations, these paired formats of RGB color
and depth map images are not always fully available. We propose a deep
learning-based method to synthesize the volumetric digital holograms using only
the given RGB image, so that we can overcome environments where RGB color and
depth map images are partially provided. The proposed method uses only the
input of RGB image to estimate its depth map and then generate its CGH
sequentially. Through experiments, we demonstrate that the volumetric hologram
generated through our proposed model is more accurate than that of competitive
models, under the situation that only RGB color data can be provided.