Unbiased, adaptive stochastic sampling for rendering inhomogeneous participating media

Realistic rendering of participating media is one of the major subjects in computer graphics. Monte Carlo techniques are widely used for realistic rendering because they provide unbiased solutions, which converge to exact solutions. Methods based on Monte Carlo techniques generate a number of light paths, each of which consists of a set of randomly selected scattering events. Finding a new scattering event requires free path sampling to determine the distance from the previous scattering event, and is usually a time-consuming process for inhomogeneous participating media. To address this problem, we propose an adaptive and unbiased sampling technique using kd-tree based space partitioning. A key contribution of our method is an automatic scheme that partitions the spatial domain into sub-spaces (partitions) based on a cost model that evaluates the expected sampling cost. The magnitude of performance gain obtained by our method becomes larger for more inhomogeneous media, and rises to two orders compared to traditional free path sampling techniques.

[1]  J. Leppänen.  Development of a New Monte Carlo reactor physics code , 2007 .

[2]  Forrest B. Brown,et al.  DIRECT SAMPLING OF MONTE CARLO FLIGHT PATHS IN MEDIA WITH CONTINUOUSLY VARYING CROSS-SECTIONS , 2003 .

[3]  Alexander Keller,et al.  Metropolis Light Transport for Participating Media , 2000, Rendering Techniques.

[4]  I. Lux.  Monte Carlo Particle Transport Methods: Neutron and Photon Calculations , 1991 .

[5]  L. L. Carter,et al.  Monte Carlo Sampling with Continuously Varying Cross Sections Along Flight Paths , 1972 .

[6]  Ken Perlin,et al.  Improving noise , 2002, SIGGRAPH.

[7]  Ingo Wald,et al.  Realtime ray tracing and interactive global illumination , 2004, Ausgezeichnete Informatikdissertationen.

[8]  Per H. Christensen,et al.  Efficient simulation of light transport in scenes with participating media using photon maps , 1998, SIGGRAPH.

[9]  Renée J. Miller,et al.  Mining for empty spaces in large data sets , 2003, Theor. Comput. Sci..

[10]  László Szirmay-Kalos,et al.  Efficient Free Path Sampling in Inhomogeneous Media , 2010, Eurographics.

[11]  Francisco J. Serón,et al.  A survey on participating media rendering techniques , 2005, The Visual Computer.

[12]  Leonidas J. Guibas,et al.  Robust Monte Carlo methods for light transport simulation , 1997 .

[13]  Alok Aggarwal,et al.  Fast algorithms for computing the largest empty rectangle , 1987, SCG '87.

[14]  Yoshinori Dobashi,et al.  Display of clouds taking into account multiple anisotropic scattering and sky light , 1996, SIGGRAPH.

[15]  W. A. Coleman.  Mathematical Verification of a Certain Monte Carlo Sampling Technique and Applications of the Technique to Radiation Transport Problems , 1968 .

[16]  Alexander Keller,et al.  Unbiased Global Illumination with Participating Media , 2008 .

[17]  Yves D. Willems,et al.  Rendering Participating Media with Bidirectional Path Tracing , 1996, Rendering Techniques.

[18]  Jos Stam,et al.  Multiple Scattering as a Diffusion Process , 1995, Rendering Techniques.

[19]  Aldo Badano,et al.  Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. , 2009, Medical physics.