US 11,748,943 B2
Cleaning dataset for neural network training
Jong Hwa Lee, San Diego, CA (US); Seunghan Kim, San Diego, CA (US); and Gary Lyons, San Diego, CA (US)
Assigned to SONY GROUP CORPORATION, Tokyo (JP)
Filed by SONY GROUP CORPORATION, Tokyo (JP)
Filed on Feb. 8, 2021, as Appl. No. 17/170,739.
Claims priority of provisional application 63/003,015, filed on Mar. 31, 2020.
Prior Publication US 2021/0303923 A1, Sep. 30, 2021
Int. Cl. G06K 9/62 (2022.01); G06T 17/00 (2006.01); G06T 7/73 (2017.01); G06T 15/04 (2011.01); G06T 7/50 (2017.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 18/214 (2023.01); G06V 10/75 (2022.01); G06V 10/74 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/64 (2022.01); G06V 40/16 (2022.01)
CPC G06T 17/00 (2013.01) [G06F 18/214 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/50 (2017.01); G06T 7/73 (2017.01); G06T 15/04 (2013.01); G06V 10/757 (2022.01); G06V 10/761 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/647 (2022.01); G06V 40/171 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An electronic device, comprising:
circuitry configured to:
receive a dataset comprising a plurality of samples,
wherein a first sample of the plurality of samples comprises a first two-dimensional (2D) image of an object of interest, a first three-dimensional (3D) shape model of the object of interest, and texture mapping information between the object of interest of the first 2D image and the first 3D shape model;
determine 2D landmarks from the first 2D image, wherein the determined 2D landmarks corresponds to shape-features of the object of interest;
extract 3D landmarks from the first 3D shape model of the received dataset;
compute a first error between the determined 2D landmarks and corresponding 2D locations of the extracted 3D landmarks on the first 2D image, based on an error metric;
determine the computed first error to be above a threshold;
update the received dataset by a removal of the first sample from the received dataset, wherein the removal is based on the determination that the computed first error is more than the threshold; and
train, based on the updated dataset, a neural network on a task of 3D reconstruction from a single 2D image.