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:
Oct. 11, 2022

Filed:

Aug. 05, 2019
Applicant:

Uatc, Llc, San Francisco, CA (US);

Inventors:

Raquel Urtasun, Toronto, CA;

Wei-Chiu Ma, Toronto, CA;

Shenlong Wang, Toronto, CA;

Yuwen Xiong, Toronto, CA;

Rui Hu, Los Alamitos, CA (US);

Assignee:

UATC, LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/246 (2017.01); G06T 7/593 (2017.01); G06T 7/00 (2017.01); G06V 10/40 (2022.01);
U.S. Cl.
CPC ...
G06T 7/246 (2017.01); G06T 7/593 (2017.01); G06T 7/97 (2017.01); G06V 10/40 (2022.01); G06T 2207/10012 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Systems, methods, tangible non-transitory computer-readable media, and devices associated with motion flow estimation are provided. For example, scene data including representations of an environment over a first set of time intervals can be accessed. Extracted visual cues can be generated based on the representations and machine-learned feature extraction models. At least one of the machine-learned feature extraction models can be configured to generate a portion of the extracted visual cues based on a first set of the representations of the environment from a first perspective and a second set of the representations of the environment from a second perspective. The extracted visual cues can be encoded using energy functions. Three-dimensional motion estimates of object instances at time intervals subsequent to the first set of time intervals can be determined based on the energy functions and machine-learned inference models.


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