This page provides a tutorial on V-Ray settings that work for most scenes.


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In this tutorial we will discuss the universal V-Ray settings.

Of course, there is no universal answer and it depends on the desired render speed and quality, but here is a set of settings that we have found to work very well for still images in many situations. Please note that these settings are not optimal, in the sense that with enough tweaking, you can probably get similar quality with faster render times. The beauty of these settings though, is that they require almost no tweaking and you are guaranteed to get a nice result in the end.  

Starting with V-Ray 3.3, the default settings in V-Ray are very close to the approach described here.

Setting the V-Ray Renderer

1. Set V-Ray as the current rendering engine (start with the default V-Ray settings).

2. In the Image sampler rollout, set the Sampler type to Adaptive or Progressive.



3. Set the Max. subdivs to 100 (one hundred). Leave the "Min. subdivs" to 1.



4. Set the primary GI engine to Brute force GI. Do not change the subdivs.

Set the secondary GI engine to Light cache.




5. Set the DMC Sampler Adaptive amount to 0.95. Typically you will need to adjust the Adaptive Threshold as the default produces too much noise. A good value is 0.005.





  • Leave all subdivs anywhere at their default values. They won't have any effect anyways - the 100 AA subdivs will almost certainly override everything else.
  • Avoid using sharpening AA filters. They can make the noise more apparent.
  • Advantages of these settings: 
    • Very little parameters for controlling render quality. 
    • Work for a very large number of scenes. 
    • Typically produce high-quality results. 
  • Disadvantages of these settings: 
    • May be slow to render. With tweaking, you may get faster results. 
  • How it works: 
    The high AA subdivs essentially causes all the sampling to be performed by the image sampler. It will take as many samples per pixel as required to achieve the specified noise threshold. In many ways, this is similar to PPT, but is done on a per-bucket basis and the number of samples is adaptive.