Lighting Virtualization Using GANs for Accurate Appearance Prediction
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Solution Overview
Problem
Consumers often face disappointment with the actual lighting appearance of purchased light fixtures due to uncertainty in how they will illuminate a space, leading to mismatched expectations regarding brightness, beam width, color temperature, and uniformity.
Innovation Solution
An AI-based lighting virtualization method using generative adversarial networks (GANs) to generate synthetic images of how light fixtures will appear in a given space before purchase, allowing users to visualize and adjust lighting parameters such as beam width, color temperature, and intensity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If consumers purchase light fixtures by guessing the spatial light appearance, then the purchasing process is simple and quick, but the actual lighting appearance often does not match consumer expectations
Solution Approach 1:
The system performs preliminary visualization of the lighting appearance in the target space before the consumer makes a purchasing decision. By generating synthetic images that show how the light fixture will actually illuminate the space, consumers can accurately predict the lighting appearance in advance, eliminating the mismatch between expected and actual performance.
2Productivity
If light fixtures are installed without prior visualization, then installation is faster and simpler, but consumers experience disappointment due to unexpected lighting appearance
Solution Approach 1:
The system generates synthetic images showing the predicted lighting appearance before installation. This preliminary visualization allows consumers to verify that the lighting will meet their expectations, thereby ensuring reliability and reducing post-installation disappointment without affecting installation speed.
3Loss of information
If synthetic images are generated using a trained GAN, then the lighting appearance can be visualized, but the images may contain artifacts and inaccuracies
Solution Approach 1:
The system uses a derived GAN that incorporates feedback mechanisms to correct and refine the synthetic images generated by the trained GAN. The derived GAN processes the initial outputs to remove artifacts and improve accuracy, ensuring that the lighting appearance information in the synthetic images is both complete and precise.
4Adaptability or versatility
If the trained GAN parameters are modified to create the derived GAN, then synthetic light appearance can be customized, but the system complexity increases
Solution Approach 1:
The system achieves light appearance customization by modifying parameters of the GAN model, specifically creating a derived GAN with adjusted parameters compared to the trained GAN. This parameter modification approach enables versatile customization of lighting appearance characteristics while maintaining a manageable system structure through controlled changes rather than complete redesign.
Data Source
AI summary
A lighting appearance virtualization method includes receiving a user image of an area, luminaire information, and light appearance information. The method further includes generating, using a trained GAN, a first synthetic image based on the user image and the luminaire information. The first synthetic image shows the luminaire in the area. The method also includes generating, using a derived GAN, a second synthetic image based on the first synthetic image. The second synthetic image shows the luminaire and a synthetic light appearance associated with the luminaire. The trained GAN is modified to derive the derived GAN, where value(s) of one or more parameters of the derived GAN are different from value(s) of the one or more corresponding parameters of the trained GAN. The synthetic light appearance depends on the values of the one or more parameters of the derived GAN.