participating media
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2021 ◽  
Author(s):  
François Desrichard ◽  
David Vanderhaeghe ◽  
Mathias Paulin
Keyword(s):  

2021 ◽  
Vol 8 (2) ◽  
pp. 177-198
Author(s):  
Wenshi Wu ◽  
Beibei Wang ◽  
Ling-Qi Yan

AbstractParticipating media are frequent in real-world scenes, whether they contain milk, fruit juice, oil, or muddy water in a river or the ocean. Incoming light interacts with these participating media in complex ways: refraction at boundaries and scattering and absorption inside volumes. The radiative transfer equation is the key to solving this problem. There are several categories of rendering methods which are all based on this equation, but using different solutions. In this paper, we introduce these groups, which include volume density estimation based approaches, virtual point/ray/beam lights, point based approaches, Monte Carlo based approaches, acceleration techniques, accurate single scattering methods, neural network based methods, and spatially-correlated participating media related methods. As well as discussing these methods, we consider the challenges and open problems in this research area.


2021 ◽  
Vol 40 (4) ◽  
pp. 1-15
Author(s):  
Cheng Zhang ◽  
Zihan Yu ◽  
Shuang Zhao

2021 ◽  
Vol 40 (4) ◽  
pp. 1-15
Author(s):  
Cheng Zhang ◽  
Zihan Yu ◽  
Shuang Zhao

2021 ◽  
Vol 40 (3) ◽  
pp. 1-15
Author(s):  
Miguel Crespo ◽  
Adrian Jarabo ◽  
Adolfo Muñoz

We present an unbiased numerical integration algorithm that handles both low-frequency regions and high-frequency details of multidimensional integrals. It combines quadrature and Monte Carlo integration by using a quadrature-based approximation as a control variate of the signal. We adaptively build the control variate constructed as a piecewise polynomial, which can be analytically integrated, and accurately reconstructs the low-frequency regions of the integrand. We then recover the high-frequency details missed by the control variate by using Monte Carlo integration of the residual. Our work leverages importance sampling techniques by working in primary space, allowing the combination of multiple mappings; this enables multiple importance sampling in quadrature-based integration. Our algorithm is generic and can be applied to any complex multidimensional integral. We demonstrate its effectiveness with four applications with low dimensionality: transmittance estimation in heterogeneous participating media, low-order scattering in homogeneous media, direct illumination computation, and rendering of distribution effects. Finally, we show how our technique is extensible to integrands of higher dimensionality by computing the control variate on Monte Carlo estimates of the high-dimensional signal, and accounting for such additional dimensionality on the residual as well. In all cases, we show accurate results and faster convergence compared to previous approaches.


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