Paper Info
Title
Photorealistic image rendering with population monte carlo energy redistribution
Abstract
This work presents a novel global illumination algorithm which concentrates computation on important light transport paths and automatically adjusts energy distributed area for each light transport path. We adapt statistical framework of Population Monte Carlo into global illumination to improve rendering efficiency. Information collected in previous iterations is used to guide subsequent iterations by adapting the kernel function to approximate the target distribution without introducing bias into the final result. Based on this framework, our algorithm automatically adapts the amount of energy redistribution at different pixels and the area over which energy is redistributed. Our results show that the efficiency can be improved by exploring the correlated information among light transport paths.
Year
DOI
Venue
2007
10.2312/EGWR/EGSR07/287-295
Rendering Techniques
Keywords
Field
DocType
population monte carlo,population monte carlo energy,statistical framework,correlated information,light transport path,different pixel,final result,novel global illumination algorithm,energy redistribution,global illumination,important light transport path,photorealistic image rendering,kernel function
Computer vision,Computer graphics (images),Computer science,Path tracing,Redistribution (cultural anthropology),Artificial intelligence,Global illumination,Pixel,Rendering (computer graphics),Metropolis light transport,Computation,Kernel (statistics)
Conference
Citations 
PageRank 
References 
14
0.87
9
Authors
4
Name
Order
Citations
PageRank
Yu-Chi Lai111717.42
ShaoHua Fan2847.48
Stephen Chenney343836.67
Charcle Dyer4140.87