AR Object Rendering with Neural Network Blur and Noise Matching

The patent applies different quality characteristics to different parts of the image: AR objects receive high-quality rendering with sharpness and clarity, while the background receives lower-quality rendering with blur and noise. This local differentiation allows AR objects to maintain rendering precision while the overall scene achieves photorealism through selective application of camera imperfections.

Instead of adding imperfections to already-rendered AR objects, the patent inverts the approach by first analyzing the background image to extract blur and noise characteristics, then using these characteristics to guide the rendering process. This ensures that AR objects are rendered with appropriate imperfections from the outset, naturally blending with the background while maintaining their visual prominence.

The patent creates a digital copy or model of the camera's imperfection characteristics by analyzing the background image to extract blur and noise parameters. This copied information is then applied to AR object rendering, avoiding the need for complex physical camera simulations while achieving realistic results through parameter transfer from the background analysis.

The patent replaces traditional mechanical or optical methods of simulating camera imperfections with neural network-based image analysis. Instead of using complex optical models or physical simulations, the system uses machine learning pipelines to automatically extract and apply blur and noise characteristics, significantly reducing computational complexity while maintaining photorealism.