US 11,710,039 B2
Systems and methods for training image detection systems for augmented and mixed reality applications
Timothy Marco, Chicago, IL (US); Joseph Voyles, Louisville, KY (US); Kyungha Lim, Naperville, IL (US); Kevin Paul, Chicago, IL (US); and Vasudeva Sankaranarayanan, Chicago, IL (US)
Assigned to PricewaterhouseCoopers LLP, New York, NY (US)
Filed by PricewaterhouseCoopers LLP, New York, NY (US)
Filed on Sep. 29, 2020, as Appl. No. 17/36,098.
Claims priority of provisional application 62/908,286, filed on Sep. 30, 2019.
Prior Publication US 2021/0097341 A1, Apr. 1, 2021
Int. Cl. G06N 3/08 (2023.01); G06V 20/20 (2022.01); G06F 18/214 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/40 (2022.01); G06V 20/64 (2022.01)
CPC G06N 3/08 (2013.01) [G06F 18/214 (2023.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06V 20/64 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A method for training a model, the method comprising:
generating a plurality of synthetic images, the generation including selecting parameters of environmental features, camera intrinsics, and a target object, the target object being a simulation of a physical object, wherein the generation of the plurality of synthetic images includes generating a greater number of synthetic images assigned high probabilistic weights than low probabilistic weights, the assigned probabilistic weights representing a likelihood of the target object being associated with the selected parameters;
annotating the plurality of synthetic images with information related to properties of the target object; and
training the model to detect the physical object using the plurality of annotated synthetic images.