Nvidia Corporation

Mark A Brophy

Stanley Thomas Birchfield

Varun Jampani

Jonathan Tremblay

Thang Hong To

David Jesus Acuna Marrero

Aayush Prakash

Cem Anil

Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.

Neural network model trained using generated synthetic images

Generation of synthetic images for training a neural network model

Growing community of inventors

Toronto, Canada

Mark A Brophy