- 专利标题: Learning to estimate high-dynamic range outdoor lighting parameters
-
申请号: US16188130申请日: 2018-11-12
-
公开(公告)号: US10936909B2公开(公告)日: 2021-03-02
- 发明人: Kalyan K. Sunkavalli , Sunil Hadap , Jonathan Eisenmann , Jinsong Zhang , Emiliano Gambaretto
- 申请人: ADOBE INC.
- 申请人地址: US CA San Jose
- 专利权人: ADOBE INC.
- 当前专利权人: ADOBE INC.
- 当前专利权人地址: US CA San Jose
- 代理机构: Shook, Hardy & Bacon L.L.P.
- 主分类号: G06K9/62
- IPC分类号: G06K9/62 ; G06K9/46 ; G06T5/00
摘要:
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.
公开/授权文献
信息查询