Rendering global light transport in real-time using machine learning

US9013496B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9013496-B2
Application numberUS-201213526716-A
CountryUS
Kind codeB2
Filing dateJun 19, 2012
Priority dateJun 19, 2012
Publication dateApr 21, 2015
Grant dateApr 21, 2015

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  5. First independent claim

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Abstract

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Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

First claim

Opening claim text (preview).

What is claimed is: 1. A method under control of one or more processors configured with a computer-readable memory device storing executable instructions, the method comprising: rendering points on surfaces in a scene that includes global light transport; determining attributes associated with each of the points, the attributes including at least a first portion of the attributes and a second portion of the attributes; training a first point regression function based on the fi…

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What does patent US9013496B2 cover?
Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained …
Who is the assignee on this patent?
Wang Jiaping, Ren Peiran, Gong Minmin, and 4 more
What technology area does this patent fall under?
Primary CPC classification G06N99/005. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Apr 21 2015 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).