Implementation of a full-color holographic system using RGB-D salient object detection and divided point cloud gridding

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 MxN 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. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Currently, a real objects-based full-color holographic system collects data using a DSLR camera array or depth camera, and reconstructs the 3-D scene of the real objects through a spatial light modulator. However, the main challenges faced by high-quality holographic 3-D display are the limited generation speed and low accuracy of computer-generated holograms. This research tackles these issues by developing an RGB-D salient object detection model for more effective and accurate point cloud data, and proposing a divided point cloud gridding method to enhance hologram generation speed.