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This page provides information on the Global DMC rollout in the V-Ray tab of the Render Settings.

Overview


Monte Carlo (MC) sampling is a method for evaluating "blurry" values (anitaliasing, depth of field, indirect illumination, area lights, glossy reflections/refractions, translucency, motion blur, etc). V-Ray uses a variant of Monte Carlo sampling called Deterministic Monte Carlo (DMC). The DMC uses a pre-defined set of samples (possibly optimized to reduce the noise), which allows re-rendering an image to always produce the exact same result. By default, the Deterministic Monte Carlo method used by V-Ray is a modification of Schlick sampling, introduced by Christophe Schlick in1 ] (see the References  section below for more information).

Instead of having separate sampling methods for each of the blurry values, V-Ray has a single unified framework that determines how many and which exact samples are to be taken for a particular value, depending on the context in which that value is required. This framework is called the DMC sampler.

The settings for the DMC sampler are located in the Global DMC rollout.

Some DMC parameters are hidden from the V-Ray 5 UI. The most optimal values for them are set by default and they do not need to be changed in the majority of setups.

However, DMC parameters as well as materials and light subdivs which are hidden from the UI can still be accessed and modified via maxscript.

 

UI Path: ||Render Setup window|| > V-Ray tab > Global DMC rollout

 

Parameters


Lock noise pattern – When enabled, the sampling pattern is the same from frame to frame in an animation. Since this may be undesirable in some cases, you can disable this option to make the sampling pattern change with time. Note that re-rendering the same frame produces the same result in both cases.

Blue noise sampling – When enabled, reorders the DMC samples in screen space to produce a more pleasing result for low sample counts.

 


References


More information on deterministic Monte Carlo sampling for computer graphics can be found from the sources listed below.

[1] C. Schlick, An Adaptive Sampling Technique for Multidimensional Integration by Ray Tracing, in Second Eurographics Workshop on Rendering (Spain), 1991, pp. 48-56
Describes deterministic MC sampling for antialiasing, motion blur, depth of field, area light sampling and glossy reflections.

[2] K. Chiu, P. Shirley and C. Wang, Multi-Jittered Sampling, in Graphics Gems IV, 1994
Describes a combination of jittered and N-rooks sampling for the purposes of computer graphics.

[3] Masaki Aono and Ryutarou Ohbuchi, Quasi-Monte Carlo Rendering with Adaptive Sampling, IBM Tokyo Research Laboratory Technical Report RT0167, November 25, 1996, pp.1-5
An online version can be found at
http://www.kki.yamanashi.ac.jp/~ohbuchi/online_pubs/eg96_html/eg96.htm
Describes an application of low discrepancy sequences to area light sampling and the global illumination problem.

[4] M. Fajardo, Monte Carlo Raytracing in Action, in State of the Art in Monte Carlo Ray Tracing for Realistic Image Synthesis, SIGGRAPH 2001 Course 21, pp. 151-162;
An online version can be found at
http://cseweb.ucsd.edu/~viscomp/classes/cse274/wi18/readings/course29sig01.pdf
Describes the ARNOLD renderer employing randomized quasi-Monte Carlo sampling using low discrepancy sequences for pixel sampling, global illumination, area light sampling, motion blur, depth of field, etc.

[5] E. Veach, December, Robust Monte Carlo Methods for Light Transport Simulation, Ph. D. dissertation for Stanford University, 1997, pp. 58-65
An online version can be found at  http://graphics.stanford.edu/papers/veach_thesis/
Includes a description of low discrepancy sequences, quasi-Monte Carlo sampling and its application to solving the global illumination problem.

[6] L. Szirmay-Kalos, Importance Driven Quasi-Monte Carlo Walk Solution of the Rendering Equation, Winter School of Computer Graphics Conf., 1998
An online version can be found at http://www.fsz.bme.hu/~szirmay/imp1_link.html
Describes a two-pass method for solving the global illumination problem employing quasi-Monte Carlo sampling, as well as importance sampling using low discrepancy sequences.