Invention Grant
- Patent Title: Deep-learned generation of accurate typical simulator content via multiple geo-specific data channels
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Application No.: US16781789Application Date: 2020-02-04
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Publication No.: US11544832B2Publication Date: 2023-01-03
- Inventor: Daniel J. Lowe , Rishabh Kaushik
- Applicant: Rockwell Collins, Inc.
- Applicant Address: US IA Cedar Rapids
- Assignee: Rockwell Collins, Inc.
- Current Assignee: Rockwell Collins, Inc.
- Current Assignee Address: US IA Cedar Rapids
- Agency: Suiter Swantz pc llo
- Main IPC: G06T11/00
- IPC: G06T11/00 ; G06T7/00 ; G06N3/04 ; G06N3/08 ; G09B9/30

Abstract:
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.
Public/Granted literature
- US20210241440A1 Deep-Learned Generation of Accurate Typical Simulator Content Via Multiple Geo-Specific Data Channels Public/Granted day:2021-08-05
Information query
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 |