Invention Application
- Patent Title: LEARNING TO ESTIMATE HIGH-DYNAMIC RANGE OUTDOOR LIGHTING PARAMETERS
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Application No.: US16188130Application Date: 2018-11-12
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Publication No.: US20200151509A1Publication Date: 2020-05-14
- Inventor: Kalyan K. Sunkavalli , Sunil Hadap , Jonathan Eisenmann , Jinsong Zhang , Emiliano Gambaretto
- Applicant: ADOBE INC.
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06K9/46

Abstract:
Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate lighting parameters for input images where the input images are synthetic and real low-dynamic range images. Such a neural network system can be trained using differences between a simple scene rendered using the estimated lighting parameters and the same simple scene rendered using known ground-truth lighting parameters. Such a neural network system can also be trained such that the synthetic and real low-dynamic range images are mapped in roughly the same distribution. Such a trained neural network system can be used to input a low-dynamic range image determine high-dynamic range lighting parameters.
Public/Granted literature
- US10936909B2 Learning to estimate high-dynamic range outdoor lighting parameters Public/Granted day:2021-03-02
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