A digital hologram-based encryption and compression method for 3D models - Sun, Yukai; Zhao, Meirong; Niu, Pingjuan; Zheng, Yelong; Liu, Chuntian; Zhang, Shiyu; Bai, Songqi; Guo, Jinwei (Frontiers in Physics, 2022)
This study proposes a novel method to compress and decompress the 3D models for safe transmission and storage. The 3D models are first extracted to become 3D point clouds, which would be classified using the K-Means algorithm. And then these nearby 3D point clouds are converted into a computer-generated hologram (CGH) by calculating the point distribution to the hologram plane using optical wavefront propagation method. The computer-generated hologram (CGH) contains the spatial coordinate information of point clouds, which can be decompressed using the Convolution Neural Network (CNN) method. The decompression accuracy of 3D point clouds is quantitatively assessed by normalized correlation coefficients (NCC) reflecting the correlation between two points and influenced by the hologram resolution, the convolution kernel, and the diffraction distance. Numerical simulations have shown that the novel method can reconstruct a high-quality 3D point cloud with an accuracy of 0.1 mm.