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US Patent 10169680 Object identification and labeling tool for training autonomous vehicle controllers

Patent 10169680 was granted and assigned to Luminar Technologies on January, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Luminar Technologies
Luminar Technologies
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Current Assignee
Luminar Technologies
Luminar Technologies
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101696800
Patent Inventor Names
Prateek Sachdeva0
Dmytro Trofymov0
Date of Patent
January 1, 2019
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Patent Application Number
159061410
Date Filed
February 27, 2018
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Patent Citations Received
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US Patent 12136127 System and method for property condition analysis
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US Patent 11487288 Data synthesis for autonomous control systems
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US Patent 11501104 Method, apparatus, and system for providing image labeling for cross view alignment
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US Patent 11508125 Navigating a virtual environment of a media content item
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US Patent 12086097 Vector computational unit
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US Patent 12103554 Systems and methods for autonomous vehicle systems simulation
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US Patent 12112447 Method for identifying, locating and tracking calibrating conduit within 3D ultrasound point cloud
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US Patent 12136030 System and method for adapting a neural network model on a hardware platform
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Patent Primary Examiner
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Ryan M Gray
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Patent abstract

Techniques for identifying and labeling distinct objects within 3-D images of environments in which vehicles operate, to thereby generate training data used to train models that autonomously control and/or operate vehicles, are disclosed. A 3-D image may be presented from various perspective views (in some cases, dynamically), and/or may be presented with a corresponding 2-D environment image in a side-by-side and/or a layered manner, thereby allowing a user to more accurately identify groups/clusters of data points within the 3-D image that represent distinct objects. Automatic identification/delineation of various types of objects depicted within 3-D images, automatic labeling of identified/delineated objects, and automatic tracking of objects across various frames of a 3-D video are disclosed. A user may modify and/or refine any automatically generated information. Further, at least some of the techniques described herein are equally applicable to 2-D images.

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