Point cloud holographic encryption display system involving 3D face recognition and air-writing.

In this study, we proposed a holographic identity verification encryption system that integrates face recognition, air-writing, and the multiple point cloud gridding encryption (M-PCGE) method to ensure multi-level security for objects. The experimental results show that the M-PCGE algorithm proposed in this paper achieves image encryption and decryption quickly with a high degree of restoration, and the security is verified.

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 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.

Full-color holographic system featuring three-dimensional salient object detection based on a U2-RAS network.

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.