Adaptive Virtual Camera Sensor for Scene-Aware Ray Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ray tracing techniques in computer graphics consume excessive processing resources and introduce latency due to indiscriminate sampling of pixels, which is undesirable in many applications.
Innovation Solution
Adaptive sampling techniques are employed to intelligently select the number of light ray samples based on foreknowledge of the scene, allowing for reduced sampling in less complex areas and utilizing texture dictionaries for post-processing denoising to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indiscriminate sampling of pixels is used in path-tracing, then image quality is improved through converging pixel values, but processing resources are consumed excessively and latency increases
Solution Approach 1:
The patent applies local quality by differentiating sampling strategies across different image regions. High-frequency regions (edges, textures) receive higher sampling rates while low-frequency regions (smooth areas) receive lower sampling rates. This is implemented through adaptive sampling that analyzes scene content and adjusts per-pixel or per-region sampling counts accordingly, resolving the contradiction by concentrating processing resources where they are most needed for convergence.
Solution Approach 2:
The patent implements dynamics through adaptive sampling that adjusts sampling rates dynamically based on scene analysis. The system evaluates scene content characteristics (edges, textures, smooth regions) and dynamically modifies sampling parameters in real-time during rendering. This dynamic adaptation allows the system to optimize the balance between convergence quality and processing efficiency for each specific scene and region.
2Measurement precision
If indiscriminate sampling of pixels is used in path-tracing, then pixel values converge to accurate representations, but latency increases which is undesirable for real-time applications
Solution Approach 1:
The patent reduces latency by applying local quality through region-specific sampling strategies. Instead of uniformly sampling all pixels, the system identifies low-frequency regions that can achieve convergence with fewer samples, thereby reducing overall rendering time. High-frequency regions maintain higher sampling rates to ensure accuracy where needed, while smooth regions benefit from reduced sampling, directly addressing the latency issue.
Solution Approach 2:
The patent applies preliminary action through scene analysis and classification performed before the actual rendering process. The system pre-evaluates scene content to identify edges, textures, and smooth regions, then pre-determines appropriate sampling rates for each region. This preliminary characterization allows the rendering to proceed with optimized sampling from the start, avoiding unnecessary sampling time and reducing latency.
3Productivity
If adaptive sampling based on scene foreknowledge is used, then processing resources are reduced, but device complexity increases due to additional sampling control mechanisms
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple regions or zones based on scene content characteristics. Each region is independently analyzed and assigned appropriate sampling rates. This segmentation approach manages complexity by breaking down the overall sampling control into manageable local decisions, making the adaptive sampling system more tractable while maintaining resource efficiency.
Solution Approach 2:
The patent implements self-service through automated scene analysis and adaptive sampling control. The system automatically characterizes scene content, identifies appropriate sampling rates, and executes optimized rendering without manual intervention. This self-service capability handles the complexity internally through automated algorithms, presenting a simplified interface to users while achieving resource efficiency through intelligent adaptive sampling.
Data Source
AI summary
Techniques associated with adaptive sampling are disclosed. In some embodiments, in response to receiving a specification of a scene to render, a sampling of each portion of the scene is determined based at least in part on the specification of the scene such that the scene is not uniformly sampled, and the scene is rendered according to the determined sampling of each portion of the scene.