US 12,236,640 B2
Machine learning based image calibration using dense fields
Jianming Zhang, Campbell, CA (US); Linyi Jin, Ann Arbor, MI (US); Kevin Matzen, San Jose, CA (US); Oliver Wang, Seattle, WA (US); and Yannick Hold-Geoffroy, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Mar. 28, 2022, as Appl. No. 17/656,796.
Prior Publication US 2023/0306637 A1, Sep. 28, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06T 7/80 (2017.01); G06T 9/00 (2006.01); G06T 11/00 (2006.01); G06V 10/764 (2022.01)
CPC G06T 7/80 (2017.01) [G06N 3/045 (2023.01); G06T 9/002 (2013.01); G06T 11/00 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 10/764 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
generating, with a dense field machine learning model, a vertical vector dense field from an input image, the vertical vector dense field comprising a vertical vector of a projected vanishing point direction for a plurality of pixels of the input image;
generating, with the dense field machine learning model, a latitude dense field from the input image, the latitude dense field comprising a projected latitude value for the plurality of pixels of the input image; and
generating, with an image processing application, at least one modification to a first image based on the vertical vector dense field and the latitude dense field, to produce an output comprising a second image.