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US Patent 10297070 3D scene synthesis techniques using neural network architectures

Patent 10297070 was granted and assigned to Inception Institute of Artificial Intelligence on May, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Inception Institute of Artificial Intelligence
Inception Institute of Artificial Intelligence
Current Assignee
Inception Institute of Artificial Intelligence
Inception Institute of Artificial Intelligence
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10297070
Patent Inventor Names
Fan Zhu0
Li Liu0
Ling Shao0
Yi Fang0
Fumin Shen0
Jin Xie0
Date of Patent
May 21, 2019
Patent Application Number
16161704
Date Filed
October 16, 2018
Patent Citations
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US Patent 10106153 Multi-network-based path generation for vehicle parking
Patent Citations Received
0
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US Patent 11055866 System and method for disparity estimation using cameras with different fields of view
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US Patent 11605168 Learning copy space using regression and segmentation neural networks
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US Patent 11600120 Apparatus for diagnosing abnormality in vehicle sensor and method thereof
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US Patent 11631260 Generation of synthetic image data using three-dimensional models
0
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US Patent 11273553 Adapting simulation data to real-world conditions encountered by physical processes
0
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US Patent 11654565 Adapting simulation data to real-world conditions encountered by physical processes
0
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US Patent 11679506 Adapting simulation data to real-world conditions encountered by physical processes
0
...
Patent Primary Examiner
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Shefali D Goradia
Patent abstract

This disclosure relates to improved techniques for synthesizing three-dimensional (3D) scenes. The techniques can utilize a neural network architecture to analyze images for detecting objects, classifying scenes and objects, and determining degree of freedom information for objects in the images. These tasks can be performed by, at least in part, using inter-object and object-scene dependency information that captures the spatial correlations and dependencies among objects in the images, as well as the correlations and relationships of objects to scenes associated with the images. 3D scenes corresponding to the images can then be synthesized using the inferences provided by the neural network architecture.

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