Patents by Inventor Alex Harvill
Alex Harvill has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20230083929Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: ApplicationFiled: November 9, 2022Publication date: March 16, 2023Applicants: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Patent number: 11532073Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: GrantFiled: July 31, 2018Date of Patent: December 20, 2022Assignees: Pixar, Disnev Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Patent number: 10740694Abstract: 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.Type: GrantFiled: August 7, 2019Date of Patent: August 11, 2020Assignee: Vis Machina Inc.Inventors: Alex Harvill, Michael Fu
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Patent number: 10706508Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: GrantFiled: July 31, 2018Date of Patent: July 7, 2020Assignees: Disney Enterprises, Inc., PixarInventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Patent number: 10699382Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: GrantFiled: July 31, 2018Date of Patent: June 30, 2020Assignees: Disney Enterprises, Inc., PixarInventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Publication number: 20200184313Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: ApplicationFiled: July 31, 2018Publication date: June 11, 2020Applicants: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Patent number: 10672109Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: GrantFiled: July 31, 2018Date of Patent: June 2, 2020Assignees: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill
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Publication number: 20200050965Abstract: 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.Type: ApplicationFiled: August 7, 2019Publication date: February 13, 2020Inventors: ALEX HARVILL, MICHAEL FU
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Publication number: 20190304067Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: ApplicationFiled: July 31, 2018Publication date: October 3, 2019Applicants: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill, David Adler
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Publication number: 20190304069Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: ApplicationFiled: July 31, 2018Publication date: October 3, 2019Applicants: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill, David Adler
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Publication number: 20190304068Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.Type: ApplicationFiled: July 31, 2018Publication date: October 3, 2019Applicants: Pixar, Disney Enterprises, Inc.Inventors: Thijs Vogels, Fabrice Rousselle, Jan Novak, Brian McWilliams, Mark Meyer, Alex Harvill