公开(公告)号：US12026830B2

公开(公告)日：2024-07-02

申请号：US17746793

申请日：2022-05-17

Applicant: NVIDIA Corporation

Inventor： Nicholas Vining ,  Paul Lalonde ,  Alexander Majercik

IPC: G06T15/50 ,  G06T5/50 ,  G06T5/70 ,  G06T15/08

CPC classification number: G06T15/506 ,  G06T5/50 ,  G06T5/70 ,  G06T15/08 ,  G06T2207/20108

Abstract: 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.

公开(公告)号：US20230377251A1

公开(公告)日：2023-11-23

申请号：US17746793

申请日：2022-05-17

Applicant: NVIDIA Corporation

Inventor： Nicholas Vining ,  Paul Lalonde ,  Alexander Majercik

IPC: G06T15/50 ,  G06T15/08 ,  G06T5/00 ,  G06T5/50

CPC classification number: G06T15/506 ,  G06T15/08 ,  G06T5/002 ,  G06T5/50 ,  G06T2207/20108

Abstract: 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.