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. 04, 2022

Filed:

Apr. 21, 2020
Applicant:

Toyota Research Institute, Inc., Los Altos, CA (US);

Inventors:

Wadim Kehl, Mountain View, CA (US);

Sergey Zakharov, Kirchseeon, DE;

Assignee:

Toyota Research Institute, Inc., Los Altos, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/58 (2022.01); G06T 7/70 (2017.01); B60W 60/00 (2020.01); G06T 7/30 (2017.01); G01S 7/48 (2006.01); G01S 17/42 (2006.01); G01S 17/89 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06V 20/584 (2022.01); B60W 60/0027 (2020.02); G01S 7/4802 (2013.01); G01S 7/4808 (2013.01); G01S 17/42 (2013.01); G01S 17/89 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/30 (2017.01); G06T 7/70 (2017.01); G06V 20/64 (2022.01); B60W 2420/42 (2013.01); B60W 2552/00 (2020.02); B60W 2554/404 (2020.02); G06T 2207/10016 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01);
Abstract

Systems and methods for three-dimensional object detection are disclosed herein. One embodiment inputs, to a neural network, a two-dimensional label associated with an object to produce a Normalized-Object-Coordinate-Space (NOCS) image and a shape vector, the shape vector mapping to a continuously traversable coordinate shape space (CSS); decodes the NOCS image and the shape vector to an object model in the CSS; back-projects, in a frustum, the NOCS image to a LIDAR point cloud; identifies correspondences between the LIDAR point cloud and the object model to estimate an affine transformation between the LIDAR point cloud and the object model; iteratively refines the affine transformation using a differentiable SDF renderer; extracts automatically a three-dimensional label for the object based, at least in part, on the iteratively refined affine transformation; and performs three-dimensional object detection of the object based, at least in part, on the extracted three-dimensional label for the object.


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