Diffuse Global Illumination Using Occlusion Plane Visibility Masks
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Solution Overview
Problem
Existing methods for computing diffuse global illumination in computer-aided graphics fail to accurately account for occlusion effects, leading to artifacts like 'popping' due to the undue influence of nearby light probes.
Innovation Solution
The solution involves subdividing the scene into regions and using occlusion planes to determine visibility masks for light probes, allowing for the weighting and selection of influential probes based on their visibility, thereby accurately calculating diffuse global illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If proximity-based probe selection is used, then computational efficiency is improved, but illumination accuracy deteriorates due to occlusion artifacts
Solution Approach 1:
The scene is divided into multiple regions using occlusion planes that pass through probe locations. These planes segment space into visible and occluded zones, allowing the system to identify which probes are actually visible from each region. This segmentation resolves the contradiction by enabling accurate probe selection (improving illumination accuracy) while still using a limited set of relevant probes (maintaining computational efficiency).
Solution Approach 2:
Occlusion planes are pre-computed during an offline phase, storing visibility information in advance. During runtime, the system only needs to query pre-stored visibility masks rather than performing complex real-time occlusion tests. This preliminary action resolves the contradiction by preparing occlusion data ahead of time (improving accuracy) while keeping runtime computations simple (maintaining efficiency).
2Reliability
If all nearby probes are considered influential, then illumination completeness is improved, but computational resource usage worsens
Solution Approach 1:
The visibility mask associated with each region provides locally optimized probe selection criteria. Instead of uniformly considering all nearby probes everywhere, the system applies region-specific visibility information to select only the subset of probes that are actually visible from each location. This local quality approach resolves the contradiction by ensuring complete illumination where needed (reliability) while minimizing unnecessary probe evaluations (computational resources).
Solution Approach 2:
The system computes visibility information for all possible probe combinations during the offline phase (excessive action), then uses this pre-computed information to select only the necessary subset during runtime (partial action). This resolves the contradiction by performing comprehensive analysis when computation is abundant (offline) and efficient selection when resources are constrained (runtime).
Data Source
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AI summary
A computer-implemented method, system and computer-readable medium for determining an illumination component for a selected point in a multi-dimensional space. The method comprises identifying a set of probes associated with the selected point, the probes located in the multi-dimensional space; for each selected one of the probes, determining which of a plurality of zones for the selected probe contains the selected point and determining visibility of said determined zone from the selected probe; and deriving an illumination component at the selected point by combining scene irradiance data associated with those of the probes from which the corresponding determined zone is determined to be visible. The illumination component being determined may be the diffuse component of global illumination as applicable to computer graphics rendering.