Visibility-Based Environment Importance Sampling for Occluded Light Transport
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Solution Overview
Problem
Existing importance sampling methods for light transport simulation in complex scenes, such as interior spaces, suffer from slow convergence due to under-sampling and the need for complex, heuristic-based importance function estimation, which is not accurate and can misguide rays, especially when light sources are occluded.
Innovation Solution
A method that generates a high-quality importance sampling function by aggregating visibility tests across rendering iterations, using a mask to identify unoccluded areas and updating the importance sampling function dynamically, combined with Multiple Importance Sampling (MIS) to improve ray tracing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional importance sampling is used to trace rays towards important elements in the environment map, then ray tracing efficiency is improved, but convergence slows down in interior scenes where light sources are occluded
Solution Approach 1:
The patent performs preliminary visibility tests from multiple shaded points to identify unoccluded environment map pixels before generating the importance sampling function. This preliminary action ensures that only actually visible light sources are included in the importance function, preventing rays from being misguided towards occluded sources and thereby maintaining both efficiency and convergence speed.
Solution Approach 2:
The patent uses visibility test results as feedback to dynamically adjust the importance sampling function. By incorporating visibility information into the importance function generation, the system adaptively redirects rays towards visible light sources, resolving the contradiction between tracing efficiency and convergence time in occluded environments.
2Measurement precision
If a high-quality importance sampling function is generated by aggregating visibility tests across rendering iterations, then accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent performs visibility tests from a selected subset of shaded points rather than all shaded points. This partial action approach generates sufficient visibility information to create an accurate importance sampling function without incurring the full computational cost of testing every point, thereby balancing accuracy with reduced overhead.
Solution Approach 2:
The patent combines visibility test results from multiple rendering iterations to aggregate a comprehensive view of visible light sources. By merging information across iterations, the system builds an increasingly accurate importance sampling function while distributing the computational load, reducing peak overhead compared to performing all tests in a single iteration.
3Reliability
If visibility tests are performed to identify unoccluded areas, then the importance sampling function becomes more accurate, but the complexity of the sampling system increases
Solution Approach 1:
The patent introduces a mask as an intermediary data structure to store visibility information from environment map pixels. This mask serves as a mediator between visibility tests and the importance sampling function, simplifying the system architecture by providing a clear separation of concerns: visibility tests populate the mask, and the mask informs the importance function generation, thereby reducing overall system complexity.
Data Source
AI summary
Systems and methods to implement a technique for determining an environment importance sampling function. An environment map may be provided where lighting information about the environment is known, but where certain pixels within a scene associated with the environment map are shaded. From these shaded pixels, rays may be drawn in random directions to determine whether the rays are occluded or can interact with the environment map, which provides an indication of a source of lighting that can be used for light transport simulations. A mask may be generated based on these occlusions and used to update the environment importance sampling function.