The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 04, 2023

Filed:

May. 28, 2020
Applicant:

Argo Ai, Llc, Pittsburgh, PA (US);

Inventors:

Hunter Goforth, Sunnyvale, CA (US);

Xiaoyan Hu, Redmond, WA (US);

Michael Happold, Pittsburgh, PA (US);

Assignee:

ARGO AI, LLC, Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06T 7/60 (2017.01); G06T 7/70 (2017.01); B60W 60/00 (2020.01); B60W 30/095 (2012.01); B60W 30/09 (2012.01); G06N 3/088 (2023.01); G06T 7/50 (2017.01); G06V 20/58 (2022.01); G06N 3/045 (2023.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01); G06V 10/20 (2022.01); G06V 10/24 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 7/73 (2017.01); B60W 30/09 (2013.01); B60W 30/0956 (2013.01); B60W 60/0016 (2020.02); B60W 60/0027 (2020.02); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/50 (2017.01); G06T 7/60 (2013.01); G06T 7/70 (2017.01); G06V 10/242 (2022.01); G06V 10/255 (2022.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30261 (2013.01);
Abstract

Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.


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