Confidence-Guided Machine Learning Image Rendering for Stable Frame Rates
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Solution Overview
Problem
Ray tracing is computationally expensive and its computational demands vary unpredictably, making it difficult to maintain a consistent frame rate and image quality in rendering processes.
Innovation Solution
Employ a machine learning system, such as a neural network, to learn the relationship between pixel surface properties and rendered pixels, allowing approximation of ray-traced images within a consistent computational budget by replacing the computationally expensive ray tracing step with a more predictable machine learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ray tracing is used to achieve realistic image rendering, then image quality is improved, but computational cost and frame rate consistency deteriorate
Solution Approach 1:
The patent pre-calculates and stores radiance values for various lighting conditions and material properties in lookup tables before rendering. During actual rendering, the system retrieves pre-computed radiance values instead of performing expensive ray tracing calculations in real-time, thus maintaining high image quality while ensuring consistent frame rates.
Solution Approach 2:
The patent creates simplified copies of complex radiance calculations by storing pre-computed radiance data in lookup tables. Instead of performing full ray tracing operations during rendering, the system uses copied radiance values from the lookup tables based on material properties and lighting conditions, significantly reducing computational cost while preserving visual quality.
2Productivity
If average image quality is set low to maintain frame rate, then frame rate consistency is improved, but image quality deteriorates
Solution Approach 1:
The patent changes the rendering approach from dynamic ray tracing with variable computational cost to a parameter-based lookup system where radiance values are determined by material properties and lighting parameters. This allows the system to maintain high image quality without the computational variability of traditional ray tracing, ensuring consistent frame rates regardless of scene complexity.
3Manufacturing precision
If average image quality is set close to maximum, then image quality is improved, but frame rate consistency deteriorates when ray tracing demands fluctuate
Solution Approach 1:
The patent pre-computes radiance values for all anticipated lighting conditions and material properties during an offline preparation phase. This preliminary action eliminates the need for expensive real-time ray tracing calculations during rendering, allowing the system to maintain maximum image quality while ensuring consistent frame rates even when scene complexity varies.
Solution Approach 2:
The patent creates a comprehensive lookup table containing copied radiance data for various lighting and material conditions. During rendering, the system retrieves appropriate radiance values from this pre-populated table based on current scene parameters, maintaining high image quality without the computational fluctuations inherent in traditional ray tracing.
Data Source
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AI summary
An image rendering method for rendering a pixel at a viewpoint comprises the steps of, for a first element of a virtual scene, having a predetermined surface at a position within that scene, evaluating whether to render a pixel corresponding to the first element using at least a first machine learning system having been trained to generate an illuminance output representative of the lighting of the predetermined surface at the position, or using an alternative rendering approach, and rendering the pixel according to which of the at least first machine learning system and the alternative rendering approach are chosen in the evaluating step; wherein the evaluating step comprises obtaining a confidence value from the at least first machine learning system indicative of the accuracy of the illuminance output, the machine learning system having been trained to generate the confidence value in conjunction with the illuminance output, and the rendering step comprises using the alternative rendering approach if the confidence value does not satisfy a predetermined threshold value.