Kwansik Kim

Direct volume rendering with shading via three-dimensional textures

A new and easy-to-implement method for direct volume rendering that uses 3D texture maps for acceleration, and incorporates directional lighting, is described. The implementation, called Voltx, produces high-quality images at nearly interactive speeds on workstations with hardware support for three-dimensional texture maps. Previously reported methods did not incorporate a light model, and did not address issues of multiple texture maps for large volumes. Our research shows that these extensions impact performance by about a factor of ten. Voltx supports orthographic, perspective, and stereo views. This paper describes the theory and implementation of this technique, and compares it to the shear-warp factorization approach. A rectilinear data set is converted into a three-dimensional texture map containing color and opacity information. Quantized normal vectors and a lookup table provide efficiency. A new tesselation of the sphere is described, which serves as the basis for normal-vector quantization. A new gradient-based shading criterion is described, in which the gradient magnitude is interpreted in the context of the field-data value and the material classification parameters, and not in isolation. In the rendering phase, the texture map is applied to a stack of parallel planes, which effectively cut the texture into many slabs. The slabs are composited to form an image.

Visualizing scalar volumetric data with uncertainty

Abstract Increasingly, more importance is placed on the uncertainty information of data being displayed. This paper focuses on techniques for visualizing 3D scalar data sets with corresponding uncertainty information at each point which is also represented as a scalar value. In Djurcilov (in: D. Ebert, J.M. Favre, R. Peikert (Eds.), Data Visualization 2001, Springer, Berlin, 2001), we presented two general methods (inline DVR approach and a post-processing approach) for carrying out this task. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. Here, we provide further improvements to those techniques primarily by showing the depth cues for the uncertainty, and also better transfer function selections.

Volume rendering data with uncertainty information

This paper explores two general methods for incorporating volumetric uncertainty information in direct volume rendering. The goal is to produce volume rendered images that depict regions of high (or low) uncertainty in the data. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. We present some initial findings on what mappings provide qualitatively satisfactory results and what mappings do not. Results are considered satisfactory if the user can identify regions of high or low uncertainty in the rendered image. We also discuss the advantages and disadvantages of both approaches.

Extended specifications and test data sets for data level comparisons of direct volume rendering algorithms

Direct volume rendering (DVR) algorithms do not generate intermediate geometry to create a visualization, yet they produce countless variations in the resulting images. Therefore, comparative studies are essential for objective interpretation. Even though image and data level comparison metrics are available, it is still difficult to compare results because of the numerous rendering parameters and algorithm specifications involved. Most of the previous comparison methods use information from the final rendered images only. We overcome limitations of image level comparisons with our data level approach using intermediate rendering information. We provide a list of rendering parameters and algorithm specifications to guide comparison studies. We extend Williams and Useltons rendering parameter list with algorithm specification items and provide guidance on how to compare algorithms. Real data are often too complex to study algorithm variations with confidence. Most of the analytic test data sets reported are often useful only for a limited feature of DVR algorithms. We provide simple and easily reproducible test data sets, a checkerboard and a ramp, that can make clear differences in a wide range of algorithm variations. With data level metrics, our test data sets make it possible to perform detailed comparison studies. A number of examples illustrate how to use these tools.

Ray-Based Data Level Comparisons of Direct Volume Rendering Algorithms

We present a new method for comparing direct volume rendering (DVR) algorithms. The motivations for this work are: the prevalence of DVR algorithms that produce slightly different images from the same data set and viewing parameters, and the limitations of existing image level comparison methods. In this paper, we describe and demonstrate the effectiveness of several ray-based metrics for data level comparison of direct volume rendering (DVR) algorithms. Unlike other papers on DVR, the focus of this paper is not on speed ups from approximations or implementations with parallel or specialized hardware, but rather on methods for comparison. However, unlike image level comparisons, where the starting point is 2D images, the main distinction of data level comparison is the use of intermediate 3D information to produce the individual pixel values during the rendering process. In addition to identifying the location and extent of differences in DVR images, these data level comparisons allow us to explain why these differences arise from different DVR algorithms. Because of the rich variety of DVR algorithms, finding a common framework for developing data level comparison metrics is one of the main challenges and contribution of this paper. In this paper, we report on how ray tracing can be used as a common framework for comparing a class of DVR algorithms.

PermWeb: remote parallel and distributed-volume visualization

In this paper we present a system for visualizing volume data from remote supercomputers. We have developed both parallel volume rendering algorithms, and the World Wide Web (WWW) software for accessing the data at the remote sites. The implementation uses Hypertext Markup Language, Java, and Common Gateway Interface scripts to connect WWW servers/clients to our volume renderers. The front ends are interactive Java classes for specification of view, shading , and classification inputs. We present performance results, and implementation details for connections to our computing resources at the University of California Santa Cruz including a MasPar MP-2, SGI Reality Engine-RE2, and SGI Challenge machines. We apply the system to the task of visualizing trabecular bone from finite element simulations. Fast volume rendering on remote compute servers through a web interface allows us to increase the accessibility of the results to more users. User interface issues, overview of parallel algorithm developments, and overall system interfaces and protocols are presented. Access is available through Uniform Resource Locator http://www.cse.ucsc.edu/research/slvg/.

DATA DEPENDENT OPTIMIZATIONS FOR PERMUTATION VOLUME RENDERING

We have developed a highly efficient, high fidelity approach for parallel volume rendering that is called permutation warping. Permutation warping may use any one pass filter kernel, an example of which is trilinear reconstruction, an advantage over the shear warp approach. This work discusses experiments in improving permutation warping using data dependent optimizations to make it more competitive in speed with the shear warp algorithm. We use a linear octree on each processor for collapsing homogeneous regions and eliminating empty space. Static load balancing is also used to redistribute nodes from a processors octree to achieve higher efficiencies. In studies on a 16384 processor MasPar MP-2, we have measured improvements of 3 to 5 times over our previous results. Run times are 73 milliseconds, 29 Mvoxels/second, or 14 frames/second for 1283 volumes, the fastest MasPar volume rendering numbers in the literature. Run times are 427 milliseconds, 39 Mvoxels/second, or 2 frames/second for 2563 volumes. The performance numbers show that coherency adaptations are effective for permutation warping. Because permutation warping has good scalability characteristics, it proves to be a superior approach for massively parallel computers when image fidelity is a required feature. We have provided further evidence for the utility of permutation warping as a scalable, high fidelity, and high performance approach to parallel volume visualization.

Data level comparison of surface classification and gradient filters

Surface classification and shading of three dimensional scalar data sets are important enhancements for direct volume rendering (DVR). However, unlike conventional surface rendering, DVR algorithms do not have explicit geometry to shade, making it difficult to perform comparisons. Furthermore, DVR, in general, involves a complex set of parameters whose effects on a rendered image are hard to compare. Previous work uses analytical estimations of the quality of interpolation, gradient filters, and classification. Typical comparisons are done using side-by-side examination of rendered images. However, non-linear processes are involved in the rendering pipeline and thus the comparison becomes particularly difficult. In this paper, we present a data level methodology for analyzing volume surface classification and gradient filters. Users can more effectively estimate algorithmic differences by using intermediate information. Based on this methodology, we also present new data level metrics and examples of analyzing differences in surface classification and gradient calculation. Please refer to www.cse.ucsc.edu/research/avis/dvr.html for a full color version of this paper.