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US Patent 11875527 Descriptor generation and point cloud fusion

Patent 11875527 was granted and assigned to Apple (company) on January, 2024 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
Apple (company)
Apple (company)
1
Current Assignee
Apple (company)
Apple (company)
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
118755271
Patent Inventor Names
Chavdar Papazov1
Lina M. Paz-Perez1
Jai Prakash1
Vimal Thilak1
Date of Patent
January 16, 2024
1
Patent Application Number
172241921
Date Filed
April 7, 2021
1
Patent Citations
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US Patent 11562560 Normal estimation for a planar surface
1
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US Patent 11589183 Inertially stable virtual auditory space for spatial audio applications
1
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US Patent 11586843 Generating training data for speed bump detection
1
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US Patent 9159166 Coordinate geometry augmented reality process for internal elements concealed behind an external element
1
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US Patent 9701015 Vision-guided robots and methods of training them
1
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US Patent 10373380 3-dimensional scene analysis for augmented reality operations
1
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US Patent 11348269 Method and apparatus for combining data to construct a floor plan
1
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US Patent 11430206 Branch-and-bound search algorithm for 4D camera localization
1
Patent Citations Received
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2
Patent Primary Examiner
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Soo Shin
1
Patent abstract

Some implementations involve a process that identifies a subset of points in an image and creates descriptors for these points. The detection and descriptor process may use one or more neural networks. In some implementations, the process includes a neural network that uses one or more fixed (e.g., weight independent) neural network layers to perform certain functions that can be performed more accurately and/or efficiently than via non-fixed (e.g., weight-based) layers. In some implementations, for example, a neural network includes a layer that determines orientation formulaically within the neural network. Such orientations may be determined convolutionally (e.g., using sliding patches) but are not determined based on internal node weights within the layer that were determined during the training of the neural network.

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