Volume Ray Casting Research Articles

Volume visualization of multicolor laser confocal microscope data

The objective of this work is to give biologists the ability to visualize multicolor laser confocal microscope (MLCM) data sets. We present the application of volume ray casting techniques to create fast and accurate visualization of the spatial structures in MLCM data sets. We provide various volume pre-processing routines for noise removal, smoothing and edge enhancement. The tool has an interactive interface to assist the operator in classifying the volume using various transparency mappings. We have developed volume rendering using volumetric ray casting based upon a perspective camera model. We illustrate an innovative way to simultaneously visualize LCM data set from three lasers using voxel level merging. A method to generate key frame animation sequence of an arbitrary fly through path is also included in the visualization tool.

Approaches that exploit vector-parallelism for three rendering and volume visualization techniques

Direct volume rendering using volume ray-casting and indirect volume rendering using the Marching Cubes isosurface extraction are popular techniques for volume visualization. Surface ray-tracing is a popular graphics technique for rendering scenes composed of well-defined surface primitives. However, these techniques are relatively computationally intensive. Thus, near-real-time computational performance is a difficult goal. This paper presents approaches to these rendering and volume visualization techniques that are tuned for efficient performance on a vector-parallel supercomputer. The approaches decompose and reconstruct the techniques to exploit inherent data parallelism and the specific characteristics of the CPU. Experimental results for several datasets are also exhibited.

Boundary cell-based acceleration for volume ray casting

Several effective acceleration techniques for volume rendering offer efficient means to skip over empty space, providing significant speedup without affecting image quality. The effectiveness of such an approach depends on its ability to accurately estimate the object boundary inside a volume with minimal computational overhead. We propose a novel boundary cell-based acceleration technique for ray casting which skips over empty space by accurately calculating the intersection distance for each ray. Very short distance estimation time is achieved by exploiting a projection template to calculate the parallel-projection values of each boundary cell and the coherency of adjacent cells. Since no hardware acceleration is used, the projection procedure can also be efficiently parallelized. Experimental results are provided to demonstrate the performance of our new algorithm.

Template-based rendering of run-length- encoded volumes

Template-based volume rendering is a technique to accelerate volume ray casting. It does not trade off image quality for rendering speed. However, it still falls short of interactive manipulation of volume data, mainly owing to the ray-by-ray volume access pattern and the long ray path in the transparent regions. In this paper we present an object-order template-based volume rendering method that uses run-length encoding to enable skipping highly transparent regions. We present three algorithms, one for each principal axis direction. By combining the advantages of object-order volume traversal and run-length encoded volumes, the algorithms achieve high quality rendering in a much shorter time than the original template-based volume rendering. © 1998 John Wiley & Sons, Ltd.

Parallel visualization algorithms: performance and architectural implications

Recently, a new class of scalable, shared-address-space multiprocessors has emerged. Like message-passing machines, these multiprocessors have a distributed interconnection network and physically distributed main memory. However, they provide hardware support for efficient implicit communication through a shared address space, and they automatically exploit temporal locality by caching both local and remote data in a processor's hardware cache. In this article, we show that these architectural characteristics make it much easier to obtain very good speedups on the best known visualization algorithms. Simple and natural parallelizations work very well, the sequential implementations do not have to be fundamentally restructured, and the high degree of temporal locality obviates the need for explicit data distribution and communication management. We demonstrate our claims through parallel versions of three state-of-the-art algorithms: a recent hierarchical radiosity algorithm by Hanrahan et al. (1991), a parallelized ray-casting volume renderer by Levoy (1992), and an optimized ray-tracer by Spach and Pulleyblank (1992). We also discuss a new shear-warp volume rendering algorithm that provides the first demonstration of interactive frame rates for a 256/spl times/256/spl times/256 voxel data set on a general-purpose multiprocessor. >

Parallel volume visualization on workstations

This paper discusses the use of general-purpose graphics workstations for interactive high-resolution volume visualization. We survey previous research results in parallel volume rendering as well as commercial products that take advantage of parallel processing to make volume rendering a practical visualization method. Our focus is on developing distributed computation methods that can distribute the memory and computational demands of volume visualization across a network of general purpose workstations. We describe three distributed computation strategies based on ray-casting volume rendering that can be implemented on either shared-memory multiprocessor workstations or on a network of ordinary workstations. Multiple views of real-time feature extraction give tremendous insight to the volume data. Multiple variable visualization helps scientists to capture the interaction between important variables in a simulation. Divide-and-conquer rendering allows interactive high-resolution volume visualization of large data sets on a network of midrange workstations, even when the data set is too large for available memory on any single workstation. Several examples in medical imaging and computational fluid dynamics are shown illustrating the practicality of these methods.