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:
Dec. 31, 2024

Filed:

Oct. 21, 2022
Applicant:

Valeo Schalter Und Sensoren Gmbh, Bietigheim-Bissingen, DE;

Inventors:

Jagdish Bhanushali, Auburn Hills, MI (US);

Peter Groth, Auburn Hills, MI (US);

Assignee:

VALEO SCHALTER UND SENSOREN GMBH, Bietigheim-Bissingen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/10 (2022.01); G06T 5/77 (2024.01); G06T 7/55 (2017.01); G06V 10/26 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); B60R 1/22 (2022.01);
U.S. Cl.
CPC ...
G06V 10/16 (2022.01); G06T 5/77 (2024.01); G06T 7/55 (2017.01); G06V 10/273 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); B60R 1/22 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01); G06V 2201/08 (2022.01);
Abstract

A method for generating an unobstructed bowl view of a vehicle that includes obtaining a plurality of images from a plurality of cameras disposed on the vehicle and determining a plurality of depth fields. The method further includes detecting a plurality of distorted objects in the plurality of images with a first machine-learned model that assigns a class distribution to the detected distorted object and estimating a distance of each distorted object from its associated camera using the plurality of depth fields. The method further includes assigning an object weight to each distorted object in the plurality of distorted objects and removing at least one distorted objects from the plurality of images. The method further includes replacing each of the at least one removed distorted objects with a representative background generated by a second machine-learned model and stitching and projecting the plurality of images to form the unobstructed bowl view.


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