Spectral and decomposition tracking for rendering heterogeneous volumes

We present two novel unbiased techniques for sampling free paths in heterogeneous participating media. Our decomposition tracking accelerates free-path construction by splitting the medium into a control component and a residual component and sampling each of them separately. To minimize expensive evaluations of spatially varying collision coefficients, we define the control component to allow constructing free paths in closed form. The residual heterogeneous component is then homogenized by adding a fictitious medium and handled using weighted delta tracking, which removes the need for computing strict bounds of the extinction function. Our second contribution, spectral tracking, enables efficient light transport simulation in chromatic media. We modify free-path distributions to minimize the fluctuation of path throughputs and thereby reduce the estimation variance. To demonstrate the correctness of our algorithms, we derive them directly from the radiative transfer equation by extending the integral formulation of null-collision algorithms recently developed in reactor physics. This mathematical framework, which we thoroughly review, encompasses existing trackers and postulates an entire family of new estimators for solving transport problems; our algorithms are examples of such. We analyze the proposed methods in canonical settings and on production scenes, and compare to the current state of the art in simulating light transport in heterogeneous participating media.

[1]  E. D. Cashwell,et al.  Evaluation of three Monte Carlo estimation schemes for flux at a point , 1977 .

[2]  H R Skullerud,et al.  The stochastic computer simulation of ion motion in a gas subjected to a constant electric field , 1968 .

[3]  Dragan Tubic,et al.  Octree indexing of DICOM images for voxel number reduction and improvement of Monte Carlo simulation computing efficiency. , 2006, Medical physics.

[4]  Jan Novák,et al.  Residual ratio tracking for estimating attenuation in participating media , 2014, ACM Trans. Graph..

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

[6]  Kei Iwasaki,et al.  Toward Optimal Space Partitioning for Unbiased, Adaptive Free Path Sampling of Inhomogeneous Participating Media , 2011, Comput. Graph. Forum.

[7]  J. Jensen Sur les fonctions convexes et les inégalités entre les valeurs moyennes , 1906 .

[8]  Kei Iwasaki,et al.  Unbiased, adaptive stochastic sampling for rendering inhomogeneous participating media , 2010, ACM Trans. Graph..

[9]  S. N. Cramer Application of the fictitious scattering radiation transport model for deep-penetration Monte Carlo calculations , 1978 .

[10]  Derek Nowrouzezahrai,et al.  Joint importance sampling of low-order volumetric scattering , 2013, ACM Trans. Graph..

[11]  S. Blanco,et al.  Radiative transfer and spectroscopic databases: A line-sampling Monte Carlo approach , 2016 .

[12]  Brent Burley,et al.  Practical and controllable subsurface scattering for production path tracing , 2016, SIGGRAPH Talks.

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

[14]  László Szirmay-Kalos,et al.  Free Path Sampling in High Resolution Inhomogeneous Participating Media , 2011, Comput. Graph. Forum.

[15]  Christopher D. Kulla,et al.  Eurographics Symposium on Rendering 2012 Importance Sampling Techniques for Path Tracing in Participating Media , 2022 .

[16]  S. R. Dwivedi Zero Variance Biasing Schemes for Monte Carlo Calculations of Neutron and Radiation Transport Problems , 1982 .

[17]  John Amanatides,et al.  A Fast Voxel Traversal Algorithm for Ray Tracing , 1987, Eurographics.

[18]  Ephraim M Sparrow,et al.  Monte carlo radiation solutions—effect of energy partitioning and number of rays , 1973 .

[19]  E. Gelbard,et al.  Monte Carlo Principles and Neutron Transport Problems , 2008 .

[20]  L. C. Henyey,et al.  Diffuse radiation in the Galaxy , 1940 .

[21]  G. Rybicki Radiative transfer , 2019, Climate Change and Terrestrial Ecosystem Modeling.

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

[23]  J. T. Ward,et al.  The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code , 1999 .

[24]  Ken Museth,et al.  VDB: High-resolution sparse volumes with dynamic topology , 2013, TOGS.

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

[26]  D. Kotlyar,et al.  Weighted-delta-tracking for Monte Carlo particle transport , 2015 .

[27]  J. Gautrais,et al.  Integral formulation of null-collision Monte Carlo algorithms , 2013 .

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

[29]  R. B. Curtis,et al.  The relativistic doppler problem , 1961 .

[30]  V. Eymet,et al.  Null-collision meshless Monte-Carlo-Application to the validation of fast radiative transfer solvers embedded in combustion simulators , 2013 .

[31]  L. B. Miller MONTE CARLO ANALYSIS OF REACTIVITY COEFFICIENTS IN FAST REACTORS GENERAL THEORY AND APPLICATIONS. , 1967 .

[32]  Jaakko Leppänen,et al.  Performance of Woodcock delta-tracking in lattice physics applications using the Serpent Monte Carlo reactor physics burnup calculation code , 2010 .

[33]  Johannes Hanika,et al.  Hero Wavelength Spectral Sampling , 2014, Comput. Graph. Forum.

[34]  H. W. Bertini,et al.  MONTE CARLO CALCULATIONS ON INTRANUCLEAR CASCADES (thesis) , 1963 .