US 12,223,670 B2
Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data
Ryan Knuffman, Danvers, IL (US); and Jeremy Carnahan, Normal, IL (US)
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed by STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed on Jul. 19, 2023, as Appl. No. 18/355,336.
Application 18/355,336 is a continuation of application No. 17/031,643, filed on Sep. 24, 2020, granted, now 11,748,901.
Claims priority of provisional application 62/972,987, filed on Feb. 11, 2020.
Prior Publication US 2023/0360244 A1, Nov. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/579 (2017.01); G06T 15/20 (2011.01)
CPC G06T 7/579 (2017.01) [G06T 15/205 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the computer system to:
receive, via one or more processors, at least one two-dimensional image corresponding to an outdoor scene, the outdoor scene including one or more outdoor objects; and
analyze, via one or more processors, the at least one two-dimensional image using a trained deep artificial neural network to generate a respective set of one or more labeled points, each of the one or more labeled points corresponding to a respective class label describing at least one of outdoor objects depicted in the two-dimensional image, wherein the respective class label indicates a type of outdoor object corresponding to the labeled point.