Ray-Tracing Denoising with Historical Reset for Dynamic Scenes
Find Innovative SolutionsGenerate Solutions
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 that fail to adapt to dynamic scenes, leading to compromised denoising quality and increased noise levels.
Innovation Solution
Implementing historical acceleration and reset techniques that utilize both normal and responsive history buffers, leveraging spatial and temporal variance to determine pixel value ranges and adapt to changing lighting conditions, thereby enhancing denoiser responsiveness and maintaining output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional temporal accumulation techniques are used in ray tracing, then denoising quality is improved, but temporal lag and ghosting artifacts increase
Solution Approach 1:
The patent implements dynamic adaptation of history buffer usage based on scene motion detection. The system switches between using history buffers (for static scenes) and current frame data (for dynamic scenes), allowing the denoising process to adapt its behavior in real-time. This dynamic switching enables the system to maintain denoising quality when needed while avoiding temporal lag when motion is detected, directly resolving the contradiction between denoising precision and temporal responsiveness.
Solution Approach 2:
The patent changes the parameter of history buffer accumulation based on detected scene dynamics. When motion is detected, the system modifies the accumulation parameter by reducing or eliminating history buffer usage. This parameter change allows the system to transition from a state that prioritizes denoising quality to one that prioritizes temporal accuracy, thereby resolving the contradiction between these two opposing requirements.
2Manufacturing precision
If traditional temporal accumulation techniques are used in ray tracing, then denoising quality is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the computational complexity of the denoising process based on scene motion detection. When motion is detected, the system simplifies the process by eliminating or reducing history buffer accumulation, thereby reducing computational complexity. When the scene is static, the system employs full history buffer accumulation to maximize denoising quality. This dynamic adjustment allows the system to optimize the balance between denoising quality and computational complexity in real-time.
Solution Approach 2:
The patent changes the computational parameter of history buffer accumulation based on scene analysis. By detecting motion and adjusting the accumulation parameter accordingly, the system can reduce computational complexity when needed while maintaining high denoising quality when the scene is static. This parameter-based control enables efficient resource utilization and resolves the contradiction between processing precision and computational overhead.
3Manufacturing precision
If history buffers are used to accumulate pixel values, then denoising is improved, but adaptability to dynamic scenes decreases
Solution Approach 1:
The patent implements a dynamic system that detects scene motion and adjusts history buffer usage accordingly. When motion is detected in a dynamic scene, the system reduces or eliminates history buffer accumulation to maintain adaptability. When the scene is static, the system activates history buffer accumulation to improve denoising quality. This dynamic adaptation mechanism enables the system to maintain both high denoising quality and adaptability to dynamic scenes by switching between different processing modes based on real-time scene analysis.
Solution Approach 2:
The system changes the parameter of history buffer accumulation based on detected scene dynamics. By monitoring scene motion and adjusting the accumulation parameter in real-time, the system can adapt to dynamic scenes while maintaining denoising quality in static scenes. This parameter-based adaptation resolves the contradiction by allowing the system to optimize its behavior according to the specific characteristics of each scene, thereby achieving both denoising precision and adaptability.
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.