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# DMC Sampler

This page provides information on the DMC Sampler rollout of the V-Ray Main render settings.

Page Contents

## Overview

Monte Carlo (MC) sampling is a method for evaluating "blurry" values (anti-aliasing, 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 difference between pure Monte Carlo sampling and deterministic Monte Carlo is that the first uses pseudo-random numbers which are different for each and every evaluation (and so re-rendering a single image will always produce slightly different results in the noise), while deterministic Monte Carlo 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 in 1991 (see the References section below).

Note that there exists a sub-set of DMC sampling called quasi Monte Carlo (QMC) sampling, in which the samples are obtained from sequences of numbers, called low-discrepancy sequences, which have special numeric properties. V-Ray, however, does not use this technique.

Instead of having separate sampling methods for each of the blurry values, V-Ray has a single unified framework that determines how many and what exactly samples 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 actual number of samples for any blurry value is determined based on three factors:

• The subdivs value entered by the user for a particular blurry effect. This is multiplied by the Subdivs Multiplier ( see below ).
• The importance of the value (for example, dark glossy reflections can do with fewer samples than bright ones, since the effect of the reflection on the final result is smaller; distant area lights require fewer samples than closer ones etc). Basing the number of samples allocated for a value on importance is called importance sampling.
• The variance (think "noise") of the samples taken for a particular value; if the samples are not very different from each other, then the value can do with fewer samples; if the samples are very different, then a larger number of them will be necessary to get a good result. This basically works by looking at the samples as they are computed one by one and deciding, after each new sample, if more samples are required. This technique is called early termination or Adaptive Sampling.

UI Path: ||V-Ray Main render settings|| > DMC Sampler rollout

## Parameters

Animated noise pattern – When enabled, the sampling pattern will change with time. Disabling will make the sampling pattern be the same from frame to frame in an animation, but this may be undesirable in some cases. Note: Re-rendering the same frame will produce the same result in both cases.

Random Seed – An initial value for the random numbers generator.

Adaptive amount – Controls the extent to which the number of samples depends on the importance of a blurry value. It also controls the minimum number of samples that will be taken. A value of 1.0 means full adaptation; a value of 0.0 means no adaptation. For most scenes, there is no need to adjust this parameter.

Adaptive threshold – Controls V-Ray's judgment of when a blurry value is "good enough" to be used. This directly translates to noise in the result. Smaller values mean less noise, more samples and higher quality. A value of 0.0 means that no adaptation will be performed. For most scenes, there is no need to adjust this parameter.

Adaptive min samples – Determines the minimum number of samples that must be made before the early termination algorithm is used. Higher values will slow things down but will make the early termination algorithm more reliable. For most scenes, there is no need to adjust this parameter.

Use Local Subdivs – When disabled , V-Ray will automatically determine subdivs values for sampling of materials, lights and other shading effects based on the Min. shading rate parameter for the image sampler. When enabled, V-Ray will use the Subdivs multiplier parameter and the Subdivs values for individual V-Ray lights and materials.

Subdivs multiplier – Multiplies all subdivs values everywhere during rendering; can be used to quickly increase/decrease sampling quality everywhere. This affects everything except for the lightmap, photon map, caustics and anti-aliasing subdivisions. Everything else (dof, moblur, irradiance map, brute-force GI, area lights, area shadows, glossy reflections/refractions) is affected by this parameter.

Divide shading subdivs – When enabled, for each image sample V-Ray divides the number of samples for lights, materials, etc. by the number of AA samples in order to achieve roughly the same quality and number of rays when changing AA settings. For example, if you have 4 AA subdivs (equals 16 image samples) and 8 light subdivs (quals 64 shadow rays), V-Ray will trace 4 (meaning 64/16) shadow rays for each image sample. This also means that in order to sample a particular blurry effect with more than one sample, its subdivs must be increased above (sometimes far above) those of the image sampler. However, some users (especially those coming from other render engines) might find this automatic division inconvenient. When disabled, the subdivs of lights, materials, etc. specify the number of subdivs for each image sample, thus allowing for more precise control of the sampling for these effects. When running the example above with this option disabled, the settings of 4 AA subdivs and 8 light subdivs will trace up to 1024 ( meaning 16*64) shadow rays, although V-Ray will still try to reduce that amount depending on the Global DMC sampler settings.The Min. shading rate parameter continues to be valid for convenience.

If you are not sure whether to enable or disable the Divide shading subdivs option, leave it enabled (its default state).

### Example: Noise vs Speed

The results in the render quality and render time are negligibly small. That is why, we highly recommend the default settings that work for a wide variety of scenes.

Adaptive amount 0,85; Noise threshold 0,005; Min samples 16

Adaptive amount 0,95 Noise threshold 0,01 Min samples 5

Adaptive amount 0,95 Noise threshold 0,05 Min samples 5

Adaptive amount 0,99 Noise threshold 0,05 Min samples 5

Adaptive amount 1 Noise threshold 0,1 Min samples 5

## References

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

• Schlick, C., 1991, An Adaptive Sampling Technique for Multidimensional Integration by Ray Tracing, in Second Eurographics Workshop on Rendering (Spain), pp. 48-56
• Describes deterministic MC sampling for anti-aliasing, motion blur, depth of field, area light sampling and glossy reflections.
• Masaki Aono and Ryutarou Ohbuchi, November 25, 1996, Quasi-Monte Carlo Rendering with Adaptive Sampling, IBM Tokyo Research Laboratory Technical Report RT0167, pp.1-5;
online version can be found here
Describes an application of low discrepancy sequences to area light sampling and the global illumination problem.
• Fajardo, M., August 13, 2001, 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;
online version can be found here
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.
• Veach, E., December, 1997, Robust Monte Carlo Methods for Light Transport Simulation, Ph. D. dissertation for Stanford University, pp. 58-65
online version can be found here
Includes a description of low discrepancy sequences, quasi-Monte Carlo sampling and its application to solving the global illumination problem.
• Szirmay-Kalos, L., 1998, Importance Driven Quasi-Monte Carlo Walk Solution of the Rendering Equation, Winter School of Computer Graphics Conf., 1998
online version can be found  here
Describes a two-pass method for solving the global illumination problem employing quasi-Monte Carlo samp ling, as well as importance sampling using low discrepancy sequences.