3D Texture Mapping Based Parallel Volume Rendering

1. Introduction

Among several rendering techniques, direct volume rendering has become an invaluable visualization technique for a wide variety of applications. Visualization of medical data such as the Visible Human, seismic data from oil or gas exploration, and computed finite element models are common examples. While direct volume rendering is very popular, software approaches are usually limited and far from interactive due to the large amount of data, expensive computations, and tremendous bandwidth requirements. As an alternative to software rendering, the use of 3D texture mapping hardware has been recognized as a very efficient acceleration technique to achieve real-time frame rates or interactive frame rate for volume rendering. Recently, large static and time-varying volume data, which consists of a sequence of 3D volume data, have been generated in various fields such as oceanography, astronomy, and computational fluid dynamics. This data is often much more difficult to visualize and accelerate because of its huge size even if 3D texture mapping hardware is available. In this project, we are going to develope hardware accelerated algorithm to render the large size data and time dependent data in real time.

2. Parallel Algorithm and Multipipe of Onyx2

• In parallel algorithm, it is important to determine how to effectively subdivide given tasks into a number of small jobs. Image-space and object-space subdivision are the most popular methods in parallel volume rendering. In image-space subdivision, various optimization techniques, such as the early ray termination and hierarchical data structure proposed for enhancing rendering speed, can be used. But, the data access pattern during parallel rendering is very irregular, which results in swapping texture memory quite often. The method is better suited for parallel rendering of volume data smaller than texture memory size. While object-space subdivision is more amenable to parallellization and can expolit the data coherence very easily, it is difficult to apply the early ray termination technique.
  
• The above figure illustrates the overview of our algorithm based upon the object-space division method to minimize texture swapping. The master pipe P0 plays an important role in controlling the slave pipes P1, P2, P3, P4, P5 and compositing sub-images. The slave pipes render assigned bricks and write sub-images on shared memory space under the control of the master pipe. And the polygonization process as a separate thread process continues to generate the sequences of polygons perpendicular to the viewing direction until the program is finished.
  
• As soon as the current view is set, a rendering order of bricks is determined. Each pipe starts creating sub-images for the assigned bricks and then the master pipe composes the sub-images according to the order. Our algorithm needs synchronization for every frame between master and slave pipes. Because the master pipe has to wait until all slave pipes write sub-images in shared memory space, the actual frame rate is affected by the slowest pipe. We tried to solve this problem using the proportional brick assignment. The same multipipe rendering scheme can be applied to not only static but also time-varying data with little modification. It is natural that the rendering of time-varying data should require much more texture swapping. To reduce the cost of texture loading, the algorithm routinely checks whether the current brick to be rendered is already stored in texture memory. We can control the rendering speed and playing direction for each timestep of time-varying data. Since our texture-based parallel algorithm is based on rendering sampling planes intersected with bricks, volume data is also considered a geometric object. So, it is easy to combine volume data with common geometric objects in an image.
  

3. Results and Images

Visible human female MRI data Shaded head with skin Data Resolution : 256x512x512	Visible human male CT data Shaded head with skin Data Resolution : 512x512x256	Visible human male CT data Shaded head with bone Data Resolution : 512x512x256
Visible human male CT data Shaded whole body with skin Data Resolution : 512x512x1294	Visible human male CT data Shaded whole body with muscle and bone Data Resolution : 512x512x1294	Visible human male CT data Shaded whole body with bone Data Resolution : 512x512x1294

Barbados data Barbados only Data Resolution : 512x256x2048	Barbados data Barbados with wells Data Resolution : 512x256x2048
Barbados data Barbados with wells Data Resolution : 512x256x2048	Barbados data Barbados Data Resolution : 512x256x2048

Visible human female CT data Data Resolution : 256x256x128	Visible human male CT data Data Resolution : 256x256x128	Visible human male CT data Data Resolution : 256x256x128

Visible human female CT data Skin only Data Resolution : 256x256x128	Visible human male CT data Skin only Data Resolution : 256x256x128	Visible human male CT data Skin only Data Resolution : 256x256x128

Visible human female CT data Bone only Data Resolution : 256x256x128	Visible human male CT data Bone only Data Resolution : 256x256x128	Visible human male CT data Bone only Data Resolution : 256x256x128

Visible human female CT data Skin and Bone Data Resolution : 256x256x128	Visible human male CT data Skin and Bone Data Resolution : 256x256x128	Visible human male CT data Skin and Bone Data Resolution : 256x256x128

4. Others

1. Technical Report Download
   
2. Texture Based Methods vs. Traditional Methods