Segmented Point Cloud Gridding Method for a Full-Color Holographic System With Real Objects

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.

Implementation of a full-color holographic system using RGB-D salient object detection

“…The author has proposed a point cloud gridding (PCG) algorithm to lift the generation speed of computer-generated holograms for real objects [24]. Then, a relocated point cloud gridding algorithm (R-PCG) is proposed; The effect of overlapping data is eliminated through approximate calculation of small amplitude layer displacement, and the reconstruction efficiency and quality of CGH of the full-color holographic system are improved, thus a color holographic display system for depth camera is constructed [25]. The author also proposed a segmented point cloud gridding algorithm to improve the hologram generation speed [26].…”