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US Patent 10169678 Object identification and labeling tool for training autonomous vehicle controllers

Patent 10169678 was granted and assigned to Luminar Technologies on January, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Luminar Technologies
Luminar Technologies
Current Assignee
Luminar Technologies
Luminar Technologies
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10169678
Date of Patent
January 1, 2019
Patent Application Number
15906676
Date Filed
February 27, 2018
Patent Citations Received
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US Patent 11933669 Optical system for improved reliability and performance
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US Patent 11508125 Navigating a virtual environment of a media content item
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US Patent 11449710 Apparatus and method for improved interface-based decision analysis
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US Patent 11885646 Programmable active pixel test injection
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US Patent 11468591 Scene attribute annotation of complex road typographies
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US Patent 10338223 Processing point clouds of vehicle sensors having variable scan line distributions using two-dimensional interpolation and distance thresholding
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US Patent 10473788 Adjusting area of focus of vehicle sensors by controlling spatial distributions of scan lines
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US Patent 10509127 Controlling vehicle sensors based on road configuration
...
Patent Primary Examiner
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Ryan M Gray
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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