monte carlo
Recently Published Documents


TOTAL DOCUMENTS

74468
(FIVE YEARS 16826)

H-INDEX

292
(FIVE YEARS 46)

2022 ◽  
Vol 169 ◽  
pp. 108824
Author(s):  
Xinyang Wang ◽  
Jingang Liang ◽  
Yulian Li ◽  
Qiong Zhang

2022 ◽  
Vol 169 ◽  
pp. 108902
Author(s):  
Guangchun Zhang ◽  
Congyu Hao ◽  
Kun Liu ◽  
Yulan Zhao ◽  
Hongchun Ding ◽  
...  

2022 ◽  
Vol 41 (1) ◽  
pp. 1-15
Author(s):  
Thomas Bashford-Rogers ◽  
Ls Paulo Santos ◽  
Demetris Marnerides ◽  
Kurt Debattista

This article proposes a Markov Chain Monte Carlo ( MCMC ) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.


Sign in / Sign up

Export Citation Format

Share Document