Ray-Traced Scene Denoising Through Temporal-Spatial History Reset
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Solution Overview
Problem
Existing image generation techniques, particularly in ray tracing, face challenges with temporal lag, ghosting, and increased computational complexity due to inefficient denoising methods, especially in dynamic scenes.
Innovation Solution
Implementing a denoising system that utilizes historical acceleration and reset mechanisms, leveraging both normal and responsive history buffers, to adapt to changing lighting conditions by adjusting convergence speeds and determining optimal reset thresholds based on spatial and temporal variances, thereby enhancing responsiveness and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional temporal accumulation techniques are used in ray tracing, then noise reduction is achieved, but temporal lag and ghosting artifacts occur
Solution Approach 1:
The patent implements dynamic history buffer management where the system adaptively adjusts the accumulation weight and reset behavior based on detected scene changes. The history buffer transition from static to dynamic state allows the system to maintain temporal accuracy while preserving noise reduction benefits, resolving the contradiction between noise reduction quality and temporal accuracy
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring pixel value changes across frames and using this information to control history buffer updates. When scene changes are detected through variance analysis, the system adjusts accumulation behavior accordingly, preventing ghosting while maintaining effective noise reduction in stable regions
2Measurement precision
If traditional temporal accumulation is applied, then denoising effect is improved, but computational complexity increases
Solution Approach 1:
The patent segments the history buffer into multiple independent channels (e.g., position, velocity, acceleration buffers) that can be processed separately. This segmentation allows parallel computation and selective updating of only necessary buffer components, reducing overall computational complexity while maintaining denoising quality
Solution Approach 2:
The system dynamically changes accumulation parameters such as blend weights and buffer sizes based on scene characteristics and performance requirements. By adjusting these parameters, the system optimizes the balance between denoising quality and computational cost, avoiding unnecessary complexity in static or simple scenes
3Measurement precision
If history buffer accumulation is used to reduce noise, then image quality improves, but responsiveness to dynamic scene changes decreases
Solution Approach 1:
The patent implements dynamic control of history buffer accumulation where the system transitions between high-accumulation and low-accumulation states based on detected scene activity. This dynamic behavior allows the system to maintain high image quality during static periods while rapidly responding to dynamic changes, resolving the contradiction between image quality and responsiveness
Solution Approach 2:
The system performs preliminary detection of scene changes using simplified metrics before full denoising processing. When changes are detected, it preemptively reduces accumulation strength or resets relevant buffer portions, preventing the lag that would otherwise occur and maintaining responsiveness while preserving image quality
Data Source
AI summary
In various examples, systems and methods are disclosed relating to historical reset. One method includes determining at least one history buffer for a frame, determining, in a spatial domain, a spatial component of the accumulated pixel value at the pixel location based on a first spatial moment and a second spatial moment, determining, in a temporal domain, a temporal component of the accumulated pixel value at the pixel location based on a first temporal moment and a second temporal moment. The method further includes determining a pixel value range based at least on the spatial component and the temporal component, determining an amount of historical reset to apply, and updating the accumulated pixel value based at least on the amount of historical reset.