Neural Network Lighting Parameter Estimation
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Solution Overview
Problem
Conventional digital imagery systems face challenges in accurately and efficiently rendering virtual objects with spatially varying lighting, often resulting in unrealistic portrayals and slow computational times due to complex computing problems and heavy network architectures.
Innovation Solution
A source-specific-lighting-estimation-neural network is employed to generate 3D lighting parameters specific to each light source, using a compact network architecture that includes common and parametric-specific layers, trained with ground-truth environment maps to accurately estimate and render spatially varying lighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional digital imagery systems use hand-crafted priors and assumed geometry to recover lighting parameters, then the system complexity is reduced, but the lighting parameters become unrealistic and inaccurate
Solution Approach 1:
The patent replaces hand-crafted priors and assumed geometry (mechanical/conventional approaches) with a neural network-based system that learns lighting parameters directly from image data. The neural network automatically extracts geometric and lighting information without relying on pre-defined models or assumptions, thereby maintaining low system complexity while achieving high lighting parameter accuracy.
2Measurement precision
If existing digital imagery systems reconstruct multi-view three-dimensional models and apply rendering-based optimization, then lighting parameter estimation becomes more accurate, but computational time increases excessively
Solution Approach 1:
The patent extracts only the essential lighting parameters directly from the input image using a neural network, without performing full multi-view three-dimensional model reconstruction or rendering-based optimization. This extraction approach achieves sufficient lighting parameter accuracy while dramatically reducing computational time by eliminating unnecessary intermediate steps.
Solution Approach 2:
The neural network is pre-trained on large datasets to learn the mapping from images to lighting parameters. During actual operation, the pre-trained network can directly predict lighting parameters without requiring time-consuming iterative optimization or reconstruction processes, thus achieving fast and accurate results.
3Measurement precision
If digital imagery systems use complex network architectures to generate spatially varying lighting parameters, then lighting accuracy improves, but the system becomes slower and more computationally demanding
Solution Approach 1:
The patent employs a dynamic neural network architecture that adapts its processing based on the input image characteristics. The network processes only the necessary features to generate spatially varying lighting parameters, avoiding unnecessary computations. This dynamic approach maintains high lighting accuracy while optimizing output speed by adjusting computational effort according to actual needs.
Data Source
AI summary
This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.