Three dimensional generative pre-trained transformer and methods of use

A method for generating secure 3D images, comprising:

• selecting and preparing proprietary datasets comprising paired RGB images and ground truth depth maps with key subject, foreground, and background elements;
• training a Three Dimensional Generative Pre-trained Transformer (3DGPT) model on said datasets using an encoder-decoder neural network with optimized loss functions;
• receiving a single 2D RGB image and generating a 3D depth map via monocular depth estimation with said trained 3DGPT model;
• integrating said 3D depth map with a Micro Optical Material (MOM) layer comprising lenticular lenses and microstructures for parallax effects;
• and
• embedding security features in said MOM layer, including at least one of micro-text, holograms, lenticular line frequency variations, encoded data.

A 3D display stack structure comprising:

• a thin-film transistor (TFT) glass layer configured for pixel control;
• a TFT polarizer coupled to said TFT glass layer for light filtering;
• a color filter (CF) glass layer with RGB sub-pixels, positioned adjacent to said TFT glass layer;
• a CF polarizer coupled to said CF glass layer for light modulation;
• a Micro Optical Material (MOM) layer comprising lenticular lenses and microstructures for parallax effects and security features;
• liquid optically clear adhesive (LOCA) bonding said TFT glass layer and said CF glass layer;
• optically clear adhesive (OCA) bonding said MOM layer to a protective cover layer;
• and
• said protective cover layer configured for stereoscopic visualization of AI-generated 3D images integrated with said security features.

A non-transitory computer-readable medium storing instructions for a Secure Pattern Recognition (SPR) application, said instructions, when executed by a processor of a mobile device, said device to:

• capture an image of a secure 3D material using a high-resolution camera;
• preprocess said image with edge detection, noise reduction, and contrast enhancement;
• analyze said image using computer vision to detect and authenticate security features embedded in a Micro Optical Material (MOM) layer, including at least one of micro-text, holograms, lenticular line frequency variations, or encoded data;
• and
• provide real-time feedback confirming authenticity based on said analysis.