Machine Learning Graphics Rendering Shading
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Solution Overview
Problem
Current computer graphics technologies face computational bottlenecks in generating high-resolution and high-frame-rate images due to the high computational demands of rendering complex scenes with multiple light sources, leading to inefficiencies and power consumption issues.
Innovation Solution
A machine-learning approach using a trained ML model to encode ambient lighting information and aggregate lighting contributions from multiple light sources into a latent representation, reducing the computational burden by performing shading operations in two separable passes and employing a denoising network to address noise in the latent representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physically-based rendering techniques are used to achieve high-quality graphics, then rendering quality is improved, but computational expense increases significantly
Solution Approach 1:
The patent segments the rendering process into distinct stages: geometry processing, rasterization, and shading. By separating these functions, the system can apply different processing strategies to each stage, using traditional GPU methods for geometry and rasterization while applying machine learning specifically to the computationally intensive shading operations, thus reducing overall computational expense while maintaining quality
Solution Approach 2:
The patent replaces the traditional mechanically-based physically-based rendering computation with a machine learning model for shading operations. The ML model learns to predict pixel colors directly from input data, substituting the complex mathematical computations of physically-based rendering with learned patterns, thereby significantly reducing computational expense while maintaining visual quality
2Manufacturing precision
If high resolution rendering is performed to achieve detailed images, then image quality is improved, but processing time increases
Solution Approach 1:
The patent applies machine learning to replace traditional rendering computations, enabling the system to process high-resolution images faster. The ML model can predict pixel colors in parallel across the entire image, avoiding the sequential nature of traditional ray-tracing and physically-based rendering, thus reducing processing time while maintaining high resolution quality
3Manufacturing precision
If complex scene rendering is performed to achieve realistic graphics, then scene detail is improved, but computational bottleneck increases
Solution Approach 1:
The patent segments the rendering pipeline to isolate the computationally intensive shading operations into a separate stage that can be handled by machine learning. This allows geometry processing and rasterization to proceed efficiently while the ML model handles complex lighting and material calculations, preventing bottlenecks in the overall rendering throughput
Solution Approach 2:
The patent substitutes traditional computationally-heavy physically-based rendering with machine learning for complex scene rendering. The ML model can process multiple light sources, materials, and geometric complexities simultaneously through learned patterns, maintaining scene detail while preventing computational bottlenecks that would otherwise limit rendering throughput
Data Source
AI summary
Embodiments described herein pertain to a machine-learning approach for shading. A system may determine a number of pixels associated with a viewpoint of a viewer. The system may determine, for each of the pixels, (1) a view direction based on the viewpoint and a pixel position of that pixel and (2) and a surface orientation of a surface visible to that pixel. The system may generate, using a first machine-learning model, a latent space representation of ambient lighting information associated with the pixels based on respective view directions and surface orientations. The system may determine color values for the pixels by processing the latent space representation of ambient lighting information using a second machine-learning model.