Aurora Operations, Inc.

Vis Machina Inc.

Michael Fu

Alex Harvill

Allen Hemberger

Virtual creation and visualization of a physically based virtual material swatch includes identifying a capture device model to support for use in the generation of the virtual material swatch, determining camera response of the capture device model, determining lookup tables of linearly transformed cosines for a plurality of parameters of a shading model, identifying a sample physical material, and determining material description for the sample physical material. A system and method may also include scanning a user physical material, selecting one or more of material preset, geometry preset, environment preset, and camera preset, and creating a virtual material swatch for the user physical material based on the selections.

Virtual creation and visualization of physically based virtual materials

A computer-implemented method of performing machine vision prediction of digital images using synthetically generated training assets comprises digitally capturing a plurality of assets; configuring each of the assets in the plurality of assets with a plurality of asset attributes; under computer program control, selecting a plurality of different combinations of parameters from among the plurality of asset attributes, and creating a plurality of sets of different synthetic dataset parameters; using computer graphics software, and example parameter values from among the synthetic dataset parameters, creating a synthetic dataset by compiling from a plurality of example images and metadata; configuring a plurality of machine learning trials and executing the trials to train a machine vision model, resulting in creating and storing a trained machine vision model; executing a validation of the trained machine vision model; and inferring a prediction using the trained machine vision model. Trained models are scored against success criteria and re-trained using pseudo-random sampling of different parameters clustered around failure points. As a result, machine vision models may be trained with high accuracy using large datasets of synthesized digital images that are richly parameterized, rather than human captured digital images.

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Albany, CA, United States of America

Michael Fu