Light Probe Rendering With Neural Denoising for Global Illumination
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Solution Overview
Problem
Existing methods for rendering virtual environments using light probes are expensive due to the need for pre-computation and storage of millions of light ray paths to achieve converged results, leading to inefficient and costly global illumination.
Innovation Solution
A system and method that utilizes a neural network to denoise lighting data captured by light probes, replacing noisy lighting data with estimated non-noisy data to improve rendering quality and reduce computational costs by generating higher quality lighting with fewer light paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If light probes are used to capture lighting information in real-time, then dynamic lighting effects can be achieved, but measurement noise degrades the quality of the captured lighting data
Solution Approach 1:
A denoising neural network is introduced as an intermediary between the noisy lighting data capture and the final lighting estimation. The network processes the noisy measurements and outputs cleaned lighting data, enabling real-time capture while maintaining high measurement precision through learned denoising patterns.
Solution Approach 2:
The system transforms the lighting data representation by applying the neural network to change the parameter quality from noisy to denoised. The network learns optimal parameter transformations during training and applies these changes in real-time operation to maintain both speed and precision.
2Measurement precision
If multiple light probes are deployed to capture comprehensive lighting information, then lighting accuracy improves, but system complexity and computational overhead increase
Solution Approach 1:
The denoising neural network serves as a computational intermediary that processes lighting data from multiple probes, enabling the system to handle complex multi-probe configurations without proportionally increasing overall system complexity. The network consolidates the processing burden into a single trained model.
Solution Approach 2:
The neural network is trained in advance on comprehensive lighting datasets to learn optimal processing patterns. This preliminary training action enables the network to efficiently handle complex multi-probe data during real-time operation, reducing the computational overhead during actual use.
3Measurement precision
If traditional denoising filters are applied to lighting data, then measurement noise is reduced, but computational performance and real-time processing capability deteriorate
Solution Approach 1:
Traditional mechanical denoising filters are replaced with a neural network-based approach. The neural network learns denoising patterns during training and applies them efficiently during inference, providing superior noise reduction while maintaining real-time processing capability that traditional filters cannot achieve.
Solution Approach 2:
The system changes the processing parameters by using a trained neural network model with optimized computational parameters. This allows the system to achieve high noise reduction while maintaining fast processing speeds, unlike traditional filters that require heavy computational resources.
Data Source
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AI summary
Methods, systems, and computer-readable media for rendering light probes in a virtual environment are disclosed. Noisy lighting data is accessed in a data structure associated with a light probe in a set of light probes in an environment. The noisy lighting data is provided as an input to a neural network. The neural network is trained to output an estimate of non-noisy lighting data based on the input. The noisy lighting data is replaced in the data structure with the estimated non-noisy lighting data.