Deep Compositional Denoising for High‐quality Monte Carlo Rendering

2021 ◽  
Vol 40 (4) ◽  
pp. 1-13
Author(s):  
Xianyao Zhang ◽  
Marco Manzi ◽  
Thijs Vogels ◽  
Henrik Dahlberg ◽  
Markus Gross ◽  
...  
Keyword(s):  
Monte Carlo ◽  
High Quality ◽  
Monte Carlo Rendering

scholarly journals Progressive Monte Carlo rendering of atmospheric flow features across scales

2017 ◽  
Vol 2 (9) ◽  
Author(s):  
Tobias Günther ◽  
Alexander Kuhn ◽  
Hans-Christian Hege ◽  
Markus Gross ◽  
Holger Theisel
Keyword(s):  
Monte Carlo ◽  
Atmospheric Flow ◽  
Monte Carlo Rendering ◽  
Flow Features

A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo rendering

2018 ◽  
Vol 34 (6-8) ◽  
pp. 765-778
Author(s):  
Jonas Deyson Brito dos Santos ◽  
Pradeep Sen ◽  
Manuel M. Oliveira
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Rendering

scholarly journals Analysis of Sample Correlations for Monte Carlo Rendering

2019 ◽  
Vol 38 (2) ◽  
pp. 473-491 ◽  
Author(s):  
Gurprit Singh ◽  
Cengiz Öztireli ◽  
Abdalla G.M. Ahmed ◽  
David Coeurjolly ◽  
Kartic Subr ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Rendering

High Quality 3C-SiC Substrate for MOSFET Fabrication

2012 ◽  
Vol 711 ◽  
pp. 91-98 ◽  
Author(s):  
Hiroyuki Nagasawa ◽  
Takamitsu Kawahara ◽  
Kuniaki Yagi ◽  
Naoki Hatta ◽  
Hidetsugu Uchida ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reduction Method ◽  
Stacking Fault ◽  
High Quality ◽  
Finite Area ◽  
Side Walls ◽  
Mc Simulation ◽  
Annihilation Probability

Quantitative efficacies of several methods for stacking fault (SF) reduction are evaluated using Monte Carlo (MC) simulation. SF density on a 3C–SiC {001} surface depends on interactions of adjoining SFs: annihilation between counter pairs of SFs and termination by orthogonal SF pairs. However, SFs are not entirely eliminated when growth occurs on undulant-Si and switch back epitaxy (SBE) due to spontaneous SF collimation that suppresses the annihilation probability of counter SFs. The MC simulation also reveals the efficacy of SF reduction method which includes the growth of 3C–SiC on finite area bounded by side walls. One can theoretically reduce the SF density below 100 cm-1on 3C–SiC {001} surface. A practical way for eliminating the SF by termination at side walls is demonstrated, and it clearly exhibits that the SF density can be reduced under 120 cm-1.

scholarly journals Efficient Monte Carlo rendering with realistic lenses

2014 ◽  
Vol 33 (2) ◽  
pp. 323-332 ◽  
Author(s):  
Johannes Hanika ◽  
Carsten Dachsbacher
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Rendering

scholarly journals Application of the Monte-Carlo Method to the Description of the Dynamics of the Spread of Salvo Pollution Complicated by Adsorption

2021 ◽  
Author(s):  
Serghei Travin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Specific Adsorption ◽  
Underlying Surface ◽  
High Quality ◽  
Kinetic Curves ◽  
The Monte Carlo Method

The possibilities of application of the Monte-Carlo method for simulating the consequences of pollutants emissions with specific adsorption on the underlying surface were considered. Effective methods of obtaining kinetic curves for the concentration of a pollutant for a selected square on the field and constructing contamination profiles for a specified time are analysed. The estimation of the necessary parameters of the model for obtaining high-quality kinetic curves was performed and recommendations for their optimization are given. Specific fronts for the spot propagation were obtained and visualised.

Orthogonal Array Sampling for Monte Carlo Rendering

2019 ◽  
Vol 38 (4) ◽  
pp. 135-147 ◽  
Author(s):  
Wojciech Jarosz ◽  
Afnan Enayet ◽  
Andrew Kensler ◽  
Charlie Kilpatrick ◽  
Per Christensen
Keyword(s):  
Monte Carlo ◽  
Orthogonal Array ◽  
Monte Carlo Rendering

scholarly journals A Spherical Gaussian Framework for Bayesian Monte Carlo Rendering of Glossy Surfaces

2013 ◽  
Vol 19 (10) ◽  
pp. 1619-1632 ◽  
Author(s):  
Ricardo Marques ◽  
Christian Bouville ◽  
Mickael Ribardiere ◽  
Luis Paulo Santos ◽  
Kadi Bouatouch
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Rendering ◽  
Bayesian Monte Carlo

Denoising in Monte Carlo rendering based on Clustered-Residual block network

2020 ◽  
Author(s):  
Yinghao Song ◽  
Chunyi Chen ◽  
Xiaojuan Hu ◽  
Haiyang Yu ◽  
Fuheng Qu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Rendering ◽  
Residual Block
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