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US Patent 10930065 Three-dimensional modeling with two dimensional data

Patent 10930065 was granted and assigned to X (company) on February, 2021 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
X (company)
X (company)
1
Current Assignee
X (company)
X (company)
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
109300651
Patent Inventor Names
Yueqi Li1
Elliott Grant1
Date of Patent
February 23, 2021
1
Patent Application Number
162971021
Date Filed
March 8, 2019
1
Patent Citations Received
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US Patent 12136030 System and method for adapting a neural network model on a hardware platform
2
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US Patent 11537811 Enhanced object detection for autonomous vehicles based on field view
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US Patent 11561791 Vector computational unit receiving data elements in parallel from a last row of a computational array
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US Patent 11562231 Neural networks for embedded devices
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US Patent 11893393 Computational array microprocessor system with hardware arbiter managing memory requests
9
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US Patent 11893774 Systems and methods for training machine models with augmented data
10
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US Patent 11983630 Neural networks for embedded devices
11
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US Patent 12020476 Data synthesis for autonomous control systems
12
...
Patent Primary Examiner
‌
Sultana M Zalalee
1
Patent abstract

Implementations are described herein for three-dimensional (“3D”) modeling of objects that target specific features of interest of the objects, and ignore other features of less interest. In various implementations, a plurality of two-dimensional (“2D”) images may be received from a 2D vision sensor. The plurality of 2D images may capture an object having multiple classes of features. Data corresponding to a first set of the multiple classes of features may be filtered from the plurality of 2D images to generate a plurality of filtered 2D images in which a second set of features of the multiple classes of features is captured. 2D-3D processing, such as structure from motion (“SFM”) processing, may be performed on the 2D filtered images to generate a 3D representation of the object that includes the second set of one or more features.

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