Abstract
The large amount of computing data from hologram calculations incurs a heavy computational load for realistic full-color holographic displays. In this research, we propose a segmented point-cloud gridding (S-PCG) method to enhance the computing ability of a full-color holographic system. A depth camera is used to collect the color and depth information from actual scenes, which are then reconstructed into the point-cloud model. Object points are categorized into depth grids with identical depth values in the red, green, and blue (RGB) channels. In each channel, the depth grids are segmented into M×N parts, and only the effective area of the depth grids will be calculated. Computer-generated holograms (CGHs) are generated from efficient depth grids by using a fast Fourier transform (FFT). Compared to the wavefront recording plane (WRP) and traditional PCG methods, the computational complexity is dramatically reduced. The feasibility of the S-PCG approach is established through numerical simulations and optical reconstructions.
Highlights
Computer-generated holography display technology has become a hot topic of threedimensional (3-D) displays (Park, 2017; Wang et al, 2017; Chen et al, 2020)
In our previous work, we have proposed a point cloud meshing (PCG) algorithm to speed up the generation speed of computer-generated holograms for real objects (Zhao et al, FIGURE 3 | Principle of the hidden point removal (HPR) operator: (A) Spherical flipping of a point cloud using a sphere centered at viewpoint V. (B) Points visible from the viewpoint
In the proposed full-color holographic system, data of the real object obtained by the depth camera, we can accurately obtain the distance from each point in the real object to the camera
Summary
Computer-generated holography display technology has become a hot topic of threedimensional (3-D) displays (Park, 2017; Wang et al, 2017; Chen et al, 2020). To realize real-time holographic video display and holographic video communication, researchers have actively studied holographic display and computer-generated hologram technology. It is difficult to collect data in real-time and calculate holograms quickly, it is difficult to meet the actual needs. The research on fast holographic algorithms for real objects and improving the adaptability of the algorithm has become an urgent problem in a computer-generated holographic 3D display (Chang et al, 2018; Wu et al, 2019). It is an unavoidable and necessary problem of computer-generated holographic displays
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