Volume Rendering

Volume Rendering Definition

Volume rendering represents a collection of methods used in computer graphics and scientific visualization to create a 2D projection from a discretely sampled 3D data set. An example of a 3D data set is a collection of MRI, CT, or MicroCT scanner 2D slice images. For instance, a series of 2D slice images of a human brain can be assembled to render 3D volume rendered images using a volume rendering algorithm.

Typically, users capture these slices with a regular number of image pixels in a sequence and at regular intervals, such as one slice per millimeter. This is called a regular volumetric grid, in which users sample the immediate area just around each voxel or volume element to obtain volumetric data and create a single representative value for that region.

Once that 3D data set is captured, rendering a 2D projection of it is next. However, it is first necessary to define the volume relative to the position of the camera in space. Users then must define each voxel's color and opacity, typically using an RGBA transfer function.

Volume Rendering example showing different levels of image visualization on a human body.
Image from Dr. Savvas Nicolaou


What is Volume Rendering?

Volume rendering allows users to visualize three-dimensional scalar fields. This is important for any industry that produces 3D data sets for analysis, including physics, medicine, disaster preparedness, and more.

Why is Volume Rendering Important to Data Visualization?

Volume rendering and the data visualization it enables allows experts to understand medical data from CAT or MRI scanners, complex fluid dynamics, data from seismic events, and other volumetric data for which geometric surfaces are unavailable—or just too difficult or cost-prohibitive to generate. Volume visualization provides a way to parse that complex data, and reveal intricate 3D relationships.

Surface Rendering vs Volume Rendering

Experts developed volume rendering techniques to overcome problems representing surfaces accurately as they sought to visualize 3D data sets. This process presents obstacles related to determining surface integrity for every volume element. Especially for data sets surrounding objects that are small or describe features that are poorly defined, determining whether the surface passes through a particular voxel can produce spurious surfaces, or false positives, and erroneous surface holes, the result of false negatives.

Volume rendering solves some of these issues presented by surface rendering techniques by abandoning the use of intermediate geometrical representations. In this way, there's no need to determine whether the surface is present or not, because weak surfaces will already be shown in the visualization.

Surface rendering relies on an assumption about the underlying structures you are visualizing from the data. In other words, you are estimating or making assumptions about what's underneath the surface based on its structure.

In volume rendering, assessing that underlying structure is part of the visualization process, and there is no such assumption. Instead, the nature of the data at each voxel is analyzed. Based on that analysis, colors and opacities are assigned, calculations are made, and the structures are visualized based on optical behavior of the components.

The critical issue for both techniques is rendering the image that is relevant for the data. Surface rendering relies on determining the surface in advance.

Volume rendering identifies and classifies relevant information, specifically colors and opacities, and assigns them to voxels based on information about them. Both high quality data and choice of technique affect volume rendering quality.

High Definition Volume Rendering

There are several steps in the volume rendering process that enable more high definition results:

  • Create an RGBA volume, a 3D four-vector data set, from the data
  • Reconstruct a continuous function using this discrete data set
  • Project the output image 2D viewing plane from the optimal point of view

An RGBA volume includes the R, G, and B color components known to most users plus A, opacity. An opacity value of 1 means completely opaque and a value of 0 means completely transparent.

The user places an opaque background behind the RGBA volume. This allows them to map the data to opacity values and classify the critical data. To show isosurfaces, map corresponding data values to values that are nearly opaque, and the rest to values that are transparent. Shading techniques can improve surface appearance to form the RGB mapping. Additionally, opacity allows users to visualize the cloudy, variable interiors of the mapped data volume.

Advantages of volume rendering include the ability to see the entire 3D data set in one piece, without discarding valuable interior information. Disadvantages of volume rendering include cost, time to perform it, and the difficulty of interpreting the kind of cloudy interiors the technique produces.

Common Volume Rendering Techniques

There is more than one volume rendering technique, and the correct one for your application depends on several factors. 3D volume rendering methods can be grouped into four categories: ray casting or raymarching, resampling or shear-warp, texture slicing, and splatting.

Direct volume rendering ray casting is typically achieved as follows. Starting with the camera, the user moves into and through the volume, gathering data on color, density, light, and gradient at each point. The end result is a slightly cloudy image that communicates details about the surveyed portion of the field. This direct volume rendering is generally better for visualizing softer data sets that represent density variations, flows fields, and related things.

For more distinct boundaries and structures, such as anything from neighborhoods to bones, it may be more useful to examine volume data using other volume rendering techniques such as 3D slicer volume rendering. In this technique, the user slices through the volume to acquire the right information at each point.

For example, a user can extract a tetrahedral or triangle mesh from the data to render it as an object that appears solid. This allows users to study the object's curvature, topology, and other physical features virtually.

The splatting method renders less accurately than techniques such as ray casting, but also works more quickly. It uses a different projection method, compositing splats in back-to-front order and on top of each other to create the final image.

Any introduction to volume rendering should cover various methods, because no one 3D volume rendering software or technique can handle every task in the same way.

3D Texture Volume Rendering

3D texture volume rendering, or simply 3D texturing or texture mapping, is a special hardware technique that allows users to generate interactive view-orthogonal slices. This generalizes and applies 2D texture-mapping to 3D textures, enabling interactive volume rendering.

In 2D texture-mapping, the user interpolates normal surface coordinates across a polygon's interior plus two additional coordinates: s and t. Texture based volume rendering interpolates three additional coordinates, s, t, and r, as indices to determine pixel opacity and color to render the 3D texture as a three-dimensional image.

What is Parallel Volume Rendering?

Parallel volume rendering enables the high-quality, interactive visualization of large datasets across a cluster of machines. There are three basic parallel volume rendering techniques. sort-first approaches place the sorting phase early in the graphics flow, before rasterizing and transforming the primitives. If sorting takes place between those steps, it's a sort-middle technique, while sorting following both other steps is a sort-last approach.

Does OmniSci Offer a Volume Rendering Solution?

OmniSci Render enables you to create interactive visualizations of high-cardinality data, server-side. OmniSci Render uses GPU buffer caching, an interface based on Vega Visualization Grammar, and modern graphics APIs to generate custom choropleths, heatmaps, pointmaps, scatterplots, and other visualizations, empowering totally scalable, zero-latency visual interaction.

Learn more about OmniSci Render and real time volume rendering solutions.