- 专利标题: Employing three-dimensional (3D) data predicted from two-dimensional (2D) images using neural networks for 3D modeling applications and other applications
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申请号: US16141649申请日: 2018-09-25
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公开(公告)号: US11263823B2公开(公告)日: 2022-03-01
- 发明人: David Alan Gausebeck , Babak Robert Shakib
- 申请人: Matterport, Inc.
- 申请人地址: US CA Sunnyvale
- 专利权人: Matterport, Inc.
- 当前专利权人: Matterport, Inc.
- 当前专利权人地址: US CA Sunnyvale
- 代理机构: Ahmann Kloke LLP
- 主分类号: G06T19/20
- IPC分类号: G06T19/20 ; H04N13/10 ; H04N13/246 ; H04N13/204 ; H04N13/106 ; H04N13/156 ; G06T7/521 ; G06T19/00 ; G06T17/00 ; G06T7/593 ; H04N13/271 ; G06T7/579 ; H04N13/00
摘要:
The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.
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