@misc{wang2012rendering,
author = {Wang, Jiaping and Ren, Peiran and Guo, Baining and Gong, Minmin and Tong, Xin and Lin, Steve},
title = {Rendering global light transport in real-time using machine learning},
year = {2012},
month = {June},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/rendering-global-light-transport-real-time-using-machine-learning/},
edition = {US Patent 9684996 B2},
note = {US Patent 9684996 B2},
}