- 专利标题: Deep-learned generation of accurate typical simulator content via multiple geo-specific data channels
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申请号: US16781789申请日: 2020-02-04
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公开(公告)号: US11544832B2公开(公告)日: 2023-01-03
- 发明人: Daniel J. Lowe , Rishabh Kaushik
- 申请人: Rockwell Collins, Inc.
- 申请人地址: US IA Cedar Rapids
- 专利权人: Rockwell Collins, Inc.
- 当前专利权人: Rockwell Collins, Inc.
- 当前专利权人地址: US IA Cedar Rapids
- 代理机构: Suiter Swantz pc llo
- 主分类号: G06T11/00
- IPC分类号: G06T11/00 ; G06T7/00 ; G06N3/04 ; G06N3/08 ; G09B9/30
摘要:
A simulator environment is disclosed. In embodiments, the simulator environment includes graphics generation (GG) processors in communication with one or more display devices. Deep learning neural networks running on the GG processors are configured for run-time generation of photorealistic, geotypical content for display. The DL networks are trained on, and use as input, a combination of image-based input (e.g., imagery relevant to a particular geographical area) and a selection of geo-specific data sources that illustrate specific characteristics of the geographical area. Output images generated by the DL networks include additional data channels corresponding to these geo-specific data characteristics, so the generated images include geotypical representations of land use, elevation, vegetation, and other such characteristics.
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IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06T | 一般的图像数据处理或产生 |
G06T11/00 | 2D〔二维〕图像的生成 |