Neural Precomputed Light Model for Consistent Frame Rates
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Solution Overview
Problem
Ray tracing is a computationally expensive process with variable computational costs, making it challenging to maintain a consistent frame rate for image rendering, as the average image quality must be compromised to manage fluctuating demands.
Innovation Solution
A machine learning system, specifically a neural precomputed light model (NPLM), is trained to learn the relationship between pixel surface properties and rendered pixels, allowing for an approximate ray-traced render with a consistent computational budget by predicting pixel values based on bidirectional scattering distribution functions (BSDFs) and light properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ray tracing is used to achieve high image quality, then image quality is improved, but computational cost and frame rate consistency deteriorate
Solution Approach 1:
The patent precomputes lighting information and stores it in a data structure before rendering. This preliminary computation of lighting effects allows the rendering system to reuse precomputed data during actual image generation, avoiding expensive real-time ray tracing calculations while maintaining high image quality and consistent frame rates.
Solution Approach 2:
The patent creates a simplified copy or representation of the lighting scene through precomputed light maps and data structures. Instead of performing complete ray tracing for each frame, the system uses these precomputed copies to approximate lighting effects, significantly reducing computational cost while preserving visual fidelity.
2Productivity
If average image quality is set low to maintain frame rate, then frame rate consistency is improved, but image quality deteriorates
Solution Approach 1:
By precomputing lighting information in advance and storing it in optimized data structures, the system eliminates the need to compromise image quality during real-time rendering. The precomputed data enables high-quality rendering at consistent frame rates without requiring quality reduction.
3Productivity
If ray tracing computational burden is reduced, then frame rate consistency is improved, but image quality and realism deteriorate
Solution Approach 1:
The system creates precomputed copies of lighting information that capture the essential visual effects of ray tracing. These copies are stored in efficient data structures that allow rapid access during rendering, maintaining visual realism while dramatically reducing computational requirements for consistent frame rates.
Solution Approach 2:
The patent transforms the rendering approach by changing parameters from real-time ray tracing to precomputed lighting with structured data access. This parameter change enables the system to achieve similar visual quality with significantly reduced computational burden, allowing consistent frame rates without sacrificing image quality.
Data Source
AI summary
A method of generating a training set for a neural precomputed light model includes: generating a plurality of candidate viewpoints of a scene, culling candidate viewpoints according to a probability that depends upon a response of the surface of the scene to light at a surface position in the scene corresponding to the viewpoint, and generating training images at the remaining viewpoints.