Nvidia Corporation

Alexander Majercik

Michael Stengel

Morgan McGuire

Ben Boudaoud

David Patrick Luebke

Dawid Stanislaw Pajak

Paul Albert Lalonde

Nicholas Vining

Morgan Mcguire

Apparatuses, systems, and techniques are presented to estimate user gaze. In at least one embodiment, one or more neural networks are used to determine coarse and fine gaze estimates for one or more users.

Gaze estimation using one or more neural networks

In order to perform denoising on a three-dimensional (3D) spherical measurement of light (such as spherical irradiance probe information or the results of a 3D gonioreflectometry capture), the 3D spherical measurement of light is converted to a two-dimensional (2D) measurement by creating multiple copies of the 3D spherical measurement of light, determining a two-dimensional sub-domain (e.g., a rectangular sub-domain) for each of the multiple copies, and stitching the plurality of two-dimensional sub-domains together in a toroidal configuration. Denoising may then be performed on this 2D measurement via a machine learning implementation or other means. This may result in more accurate 3D spherical light probes that require fewer light measurement samples to generate accurate light measurements.

Performing spherical denoising

A remote device utilizes ray tracing to compute a light field for a scene to be rendered, where the light field includes information about light reflected off surfaces within the scene. This light field is then compressed utilizing one or more video compression techniques that implement temporal reuse, such that only differences between the light field for the scene and a light field for a previous scene are compressed. The compressed light field data is then sent to a client device that decompresses the light field data and uses such data to obtain the light field for the scene at the client device. This light field is then used by the client device to compute global illumination for the scene. The global illumination may be used to accurately render the scene at the mobile device, resulting in a realistic scene that is presented by the mobile device.

Streaming a compressed light field

A remote device utilizes ray tracing to compute a light field for a scene to be rendered, where the light field includes information about light reflected off surfaces within the scene. This light field is then compressed utilizing lossless or lossy compression and one or more video compression techniques that implement temporal reuse, such that only differences between the light field for the scene and a light field for a previous scene are compressed. The compressed light field data is then sent to a client device that decompresses the light field data and uses such data to obtain the light field for the scene at the client device. This light field is then used by the client device to compute global illumination for the scene. The global illumination may be used to accurately render the scene at the mobile device, resulting in a realistic scene that is presented by the mobile device.

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San Francisco, CA, United States of America

Alexander Majercik