Volume Rendering: Exploring Visual Realism in Computer Vision

By Fouad Sabry

What is Volume Rendering

In scientific visualization and computer graphics, volume rendering is a set of techniques used to display a 2D projection of a 3D discretely sampled data set, typically a 3D scalar field.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Volume rendering

Chapter 2: Rendering (computer graphics)

Chapter 3: Texture mapping

Chapter 4: Voxel

Chapter 5: Tomography

Chapter 6: Ray casting

Chapter 7: Scientific visualization

Chapter 8: Reyes rendering

Chapter 9: Clipping (computer graphics)

Chapter 10: Volume ray casting

(II) Answering the public top questions about volume rendering.

(III) Real world examples for the usage of volume rendering in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Volume Rendering.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 14, 2024

Book preview

Chapter 1: Volume rendering

Volume rendering is a set of techniques used in scientific visualization and computer graphics to present a 2D projection of a 3D discretely sampled data set, often a 3D scalar field.

A collection of 2D slice pictures recorded by a CT, MRI, or MicroCT scanner constitutes a typical 3D data set. Typically acquired in a regular pattern (e.g., one slice per millimeter of depth) and with a regular amount of picture pixels in a regular pattern. This is an example of a regular volumetric grid in which each volume element, or voxel, is represented by a single value derived by sampling the area immediately surrounding the voxel.

To create a 2D projection of a 3D data collection, a camera must be defined in space relative to the volume. Additionally, one must determine the opacity and color of each voxel. This is typically defined using an RGBA (red, green, blue, alpha) transfer function, which specifies the RGBA value for each voxel value.

A volume may be viewed, for instance, by extracting isosurfaces (surfaces with equal values) from the volume and drawing them as polygonal meshes, or by rendering the volume directly as a block of data. A typical method for extracting an isosurface from volume data is the marching cubes algorithm. Direct volume rendering is a computationally costly process that can be accomplished in a number of ways.

Volume rendering is distinct from thin slice tomography presentations as well as projections of 3D models, such as maximum intensity projection. In order to make realistic or perceptible depictions, it is necessary to:.

Every sample value in a direct volume renderer must be mapped to opacity and color. This is accomplished by the use of a transfer function, which may be a basic ramp, a piecewise linear function, or an arbitrary table. After being transformed to an RGBA value (for red, green, blue, and alpha), the RGBA output is projected onto the appropriate pixel of the frame buffer. This depends on the rendering technique employed.

It is feasible for these techniques to be combined. For example, a shear warp implementation could utilize texturing hardware to render aligned slices in the off-screen buffer.

The volume ray casting approach can be obtained directly from the rendering equation. It produces extremely high-quality images and is typically regarded as offering the highest image quality. Volume ray casting is categorized as an image-based volume rendering technique because the calculation is derived from the output image rather than the input volume data, as with object-based techniques. In this method, a ray is generated for each image pixel that is wanted. Using a simple camera model, the ray begins at the projection center of the camera (often the eye point) and goes through the image pixel on an artificial image plane floating between the camera and the volume to be produced. In order to save time, the ray is clipped by the bounds of the volume. Then, the ray is sampled throughout the volume at regular or adaptive intervals. At each sample location, the data is interpolated, the transfer function is applied to generate an RGBA sample, the sample is composited onto the ray's accumulated RGBA, and the process is repeated until the ray exits the volume. The RGBA color is transformed to an RGB color and stored in the pixel corresponding to that color. The procedure is repeated for each pixel on the display to create the final image.

This method sacrifices quality for velocity. Here, each volume element is splattered on the viewing surface in reverse order, as described by Lee Westover. These splatters are displayed as disks with radically varying (Gaussian) hue and opacity. Depending on the application, flat disks and those with various types of property distribution are also utilized.

Cameron and Undrill created the shear warp approach to volume rendering, which was popularized by Philippe Lacroute and Marc Levoy. This technique transforms the viewing transformation such that the nearest face of the volume is axis aligned with an off-screen picture data buffer with a fixed voxel-to-pixel scale. The volume is then displayed into this buffer using memory alignment and scaling and blending parameters that are significantly more advantageous. Once all volume slices have been produced, the buffer is warped into the correct orientation and the displayed image is resized.

Compared to ray casting, this technique is comparatively fast in software at the expense of less accurate sampling and perhaps inferior image quality. Multiple copies of the volume must be stored in memory for the ability to have volumes aligned close to the axis. This burden can be reduced by run length encoding.

Numerous 3D graphics systems apply images or textures to geometric objects via texture mapping. Standard PC graphics cards are quick at texturing and can create 3D volume slices with real-time interaction capabilities. Workstation GPUs are the foundation for the majority of production volume visualization used in the medical imaging, oil and gas, and other industries (2007). In the past, dedicated 3D texture mapping methods were utilized on graphics systems such as Silicon Graphics InfiniteReality, HP Visualize FX, and others. This method was initially described by Bill Hibbard and David Santek.

These slices may be aligned with the volume and presented at an angle to the viewer, or they may be aligned with the viewing plane and sampled from non-aligned slices through the volume. Graphics hardware that supports 3D textures is required for the second method.

The images produced by volume-aligned texturing are of acceptable quality, although there is frequently a discernible transition when the volume is rotated.

Due to the highly parallel nature of direct volume rendering, specialized volume rendering hardware was a popular research area prior to the advent of GPU volume rendering. The 2007 VolumePro real-time ray-casting system, developed by Hanspeter Pfister and experts at Mitsubishi Electric Research Laboratories, was the most often referenced technology.

Utilizing contemporary graphics cards is a recently implemented way for accelerating traditional volume rendering algorithms such as ray-casting. Beginning with programmable pixel shaders, people realized the power of parallel operations on many pixels and began performing general-purpose computing on graphics processing units (GPGPU). Pixel shaders are capable of randomly reading and writing from video memory and performing certain elementary mathematical and logical operations. These SIMD processors were utilized for general calculations, including polygon rendering and signal processing. Recent GPU versions enable pixel shaders to operate as MIMD processors (now capable of independent branching) employing up to 1 GB of texture memory with floating point formats. With such processing capacity, nearly any algorithm with parallelizable phases, such as volume ray casting or