Adaptive Ray-Tracing Denoising via Uncertainty Prediction
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Solution Overview
Problem
Conventional ray-tracing techniques require a large number of samples for accurate rendering, leading to inefficient use of computational resources and significant delays in producing high-quality images, as they often generate unnecessary samples in areas that do not need them.
Innovation Solution
An adaptive rendering process using a deep neural network to predict a denoised image and uncertainty map, distributing additional samples based on predicted uncertainty, allowing for iterative refinement until a completion criterion is met, thereby optimizing sample distribution and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of ray-traced samples are used for each pixel, then the accuracy of lighting conditions is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent applies adaptive sampling that distributes samples non-uniformly across the image based on local noise characteristics. Areas with high variance or complex lighting receive more samples, while uniform areas receive fewer samples. This resolves the contradiction by maintaining high accuracy only where needed rather than uniformly across the entire image.
Solution Approach 2:
The patent uses a two-stage approach: first generating a preliminary render with sparse sampling, then applying denoising algorithms that selectively refine only the necessary regions. This partial action approach achieves acceptable accuracy for most areas while using excessive sampling only locally where required, balancing speed and quality.
2Productivity
If adaptive sampling is used to distribute samples across different pixels, then the sampling budget is optimized, but the accuracy of sample allocation is limited
Solution Approach 1:
The patent implements feedback loops where each rendering pass evaluates the quality of the previous pass and uses this information to guide sample distribution in subsequent passes. Denoising algorithms analyze noise patterns and feed this information back into the sampling strategy, continuously improving allocation accuracy based on actual rendering results rather than static heuristics.
Solution Approach 2:
The patent performs preliminary analysis of the scene to identify regions that will require high-quality rendering before the main rendering process begins. This preliminary action allows the system to pre-allocate samples to critical areas, ensuring accurate sample distribution from the start rather than discovering allocation needs during the rendering process.
3Measurement precision
If conventional denoising techniques are applied to reduce noise, then the visual quality is improved, but computational resources are wasted on areas that do not need refinement
Solution Approach 1:
The patent applies denoising operations selectively to only those regions identified as needing refinement, rather than processing the entire image uniformly. By analyzing noise metrics and applying denoising algorithms only to high-variance regions, the system achieves visual quality improvement while minimizing computational waste in already-clean areas.
Solution Approach 2:
The patent uses a multi-pass approach where denoising is applied iteratively to progressively refine only the necessary portions of the image. Each pass applies partial denoising action to regions that still exhibit noise, stopping when quality thresholds are met, rather than applying excessive denoising to the entire image regardless of need.
Data Source
AI summary
Various techniques for adaptive rendering of images with noise reduction are described. More specifically, the present disclosure relates to approaches for rendering and denoising images—such as ray-traced images—in an iterative process that distributes computational efforts to pixels where denoised output is predicted with higher uncertainty. In some embodiments, an input image may be fed into a deep neural network (DNN) to jointly predict a denoised image and an uncertainty map. The uncertainty map may be used to create a distribution of additional samples (e.g., for one or more samples per pixel on average), and the additional samples may be used with the input image to adaptively render a higher quality image. This process may be repeated in a loop, until some criterion is satisfied, for example, when the denoised image converges to a designated quality, a time or sampling budget is satisfied, or otherwise.