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.

Recently, a real objects-based full-color holographic display system usually uses a DSLR camera array or depth camera to collect data, and then relies on a spatial light modulator to modulate the input light source for the reconstruction of the 3D scene from the real objects. The main challenges faced by the holographic 3D display were introduced, including limited generation speed and accuracy of the computer-generated holograms, the imperfect performance of the holographic display system. In this research, we generated more effective and accurate point cloud data by developing a 3D saliency detection model in the acquisition module. Object points categorized into depth girds with identical depth values in the red, green, and blue (RGB) channels. In each channel, the depth girds 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 Fast Fourier transform (FFT). Compared to the wave-front recording plane (WRP) and traditional PCG methods, the computational complexity is dramatically reduced. The feasibility of the proposed approaches is established through experiments.

At present, a real objects-based full-color holographic system usually uses a digital single-lens reflex (DSLR) camera array or depth camera to collect data. It then relies on a spatial light modulator to modulate the input light source for the reconstruction of the 3-D scene of the real objects. However, the main challenges the high-quality holographic 3-D display faced were the limitation of generation speed and the low accuracy of the computer-generated holograms. This research generates more effective and accurate point cloud data by developing an RGB-D salient object detection model in the acquisition unit. In addition, a divided point cloud gridding method is proposed to enhance the computing speed of hologram generation. In the RGB channels, we categorized each object point into depth grids with identical depth values. The depth girds are divided into M × N parts, and only the effective parts will be calculated. Compared with traditional methods, the calculation time is dramatically reduced. The feasibility of our proposed approach is established through experiments.

We propose a three-dimensional significant object detection method to improve the quality of the reconstructed image in the full color holographic system. The feasibility of this method is verified from the numerical experiments.

In this study, a 3D salient object detection model is built at the acquisition step in the full-color holographic system, and a deep network architecture U 2 -reverse attention and residual learning (RAS) algorithm is proposed for salient object detection to obtain more efficient and accurate point cloud information. In addition, we also use the point cloud gridding method to improve the hologram generation speed. Compared with the traditional region of interest method, RAS algorithm, and U 2 -Net algorithm, the computational complexity is significantly reduced. Finally, the feasibility of this method is proved by experiments.