US 11,995,805 B2
Imputation of 3D data using generative adversarial networks
Ryan Knuffman, Danvers, 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 Nov. 7, 2022, as Appl. No. 17/982,174.
Application 17/982,174 is a continuation of application No. 17/031,580, filed on Sep. 24, 2020, granted, now 11,508,042.
Claims priority of provisional application 62/967,315, filed on Jan. 29, 2020.
Prior Publication US 2023/0060097 A1, Feb. 23, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 5/00 (2006.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 5/77 (2024.01); G06T 7/579 (2017.01)
CPC G06T 5/77 (2024.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/579 (2017.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A non-transitory computer readable storage medium having stored thereon instructions that, when executed by one or more processors, cause a computer to:
generate a loss value by processing one or more three-dimensional regions and at least one three-dimensional point cloud;
update one or more weights of a generative adversarial network by backpropagating the loss value;
store the updated weights of the generative adversarial network on a non-transitory computer readable storage medium;
obtain a three-dimensional point cloud having one or more gaps;
initialize the generative adversarial network using the stored weights; and
impute one or both of (i) RGB data, and (ii) elevation data into the gaps of the three-dimensional point cloud by analyzing the three-dimensional point cloud using the initialized generative adversarial network.