This page contains information about the Raytrace and Antialiasing settings.
In V-Ray, an image sampler is an algorithm for sampling and filtering the image function. It produces the final array of pixels that constitute the rendered image. V-Ray for Rhino implements two algorithms for sampling an image: Progressive and Bucket.
The Raytrace rollout provides controls related to sampling methods and anti-aliasing filters.
Example: What is Anti-aliasing?
The following example shows the basic difference between an image with anti-aliasing, and one without:
The left images are jagged around the edges of the sphere, while the right are smooth. Here are close-ups of the two images:
Example: Anti-Aliasing Filters
This example briefly demonstrates the effect of different anti-aliasing filters on the final result.
Note that rendering with a particular filter is not the same as rendering without a filter and then blurring the image in a post-processing program like Adobe Photoshop. Filters are applied on a sub-pixel level, over the individual sub-pixel samples. Therefore, applying the filter at render time produces a much more accurate and subtle result than applying it as a post effect.
|Filtering is off||Applies an internal 1x1 pixel box filter.|
|Slightly blurs the image, visually more pleasing than the box filter.|
|Edge-enhancing filter, often used for architectural visualizations. Note that edge enhancing can produce "moire" effects on detailed geometry.|
Example: Anti-Aliasing Filters and Moire Effects
This example demonstrates the effect anti-aliasing filters have on moire effects in images. Sharpening filters (e.g. Catmull-Rom) may enhance moire effects, even if your image sampling rate is very high. Blurring filters (e.g. Area) reduce moire effects.
Note that moire effects are not necessarily a result of poor image sampling. In general, moire effects appear simply because the image is discretized into square pixels. As such, they are inherent to digital images. The effect can be reduced through the usage of different anti-aliasing filters, but is not completely avoidable.
The scene is very simple: a sphere with a very fine checker map applied, texture filtering is off. The images were rendered with a very high sampling rate (15 subdivs, or 225 rays/pixel). This is enough to produce quite an accurate approximation to the pixel values. Note that the image looks quite different depending on the filter:
Area filter, size = 1.5
Area filter, size = 4.0
Triangle Filter, size = 1.5
Using Progressive or Non-Progressive rendering
No one image sampler is best for all scenes or workflows. Choosing the best image sampler is usually a matter of experimentation, but there are a few guidelines you can follow.
- Disabling the Progressive toggle is the same as using the the Adaptive image sampler type in previous versions of V-Ray. When Progressive is disabled, the image is rendered in buckets.
- Progressive is useful when it is necessary to see overall results quickly (like when placing light, building shaders, or general Look Development work) because it generates the whole image at once and progressively cleans up the noise in it. Additionally, the render can be stopped at any time before resolving completely.
- Progressive is also helpful when a set amount of time to spend per render is needed. This can also be useful when rendering test animations, where the entire sequence must be rendered within a certain time frame.
- Combining the VRayDenoiser with Progressive renders can help with cleaning up render noise.
- Disabling the Progressive toggle when Swarm is Enabled can be helpful for cutting down on network traffic and reducing the loss of information if work is not completed by one or more Swarm nodes.
Image Samplers and RAM Usage
Image samplers require a substantial amount of RAM to store render information. This is especially true for the Progressive sampler, which stores the entire image in memory before beginning the rendering process. The Bucket sampler, on the other hand, stores only the summed result of all sub-samples for a pixel and so usually requires less RAM. Using large bucket sizes might require more memory.