DevLan/vispubs huggingface dataset:
Isosurface Extraction and View-Dependent filtering from Time-Varying fields Using Persistent Time-Octree (PTOT)
Authors: Cong Wang, Yi-Jen Chiang
Published at Vis, 2009

We develop a new algorithm for isosurface extraction and view-dependent filtering from large time-varying fields, by using a novel persistent time-octree (PTOT) indexing structure. Previously, the persistent octree (POT) was proposed to perform isosurface extraction and view-dependent filtering, which combines the advantages of the interval tree (for optimal searches of active cells) and of the branch-on-need octree (BONO, for view-dependent filtering), but it only works for steady-state(i.e., single time step) data. For time-varying fields, a 4D version of POT, 4D-POT, was proposed for 4D isocontour slicing, where slicing on the time domain gives all active cells in the queried timestep and isovalue. However, such slicing is not output sensitive and thus the searching is sub-optimal. Moreover, it was not known how to support view-dependent filtering in addition to time-domain slicing.In this paper, we develop a novel persistent time-octree (PTOT) indexing structure, which has the advantages of POT and performs 4D isocontour slicing on the time domain with an output-sensitive and optimal searching. In addition, when we query the same iso value q over m consecutive time steps, there is no additional searching overhead (except for reporting the additional active cells) compared to querying just the first time step. Such searching performance for finding active cells is asymptotically optimal, with asymptotically optimal space and preprocessing time as well. Moreover, our PTOT supports view-dependent filtering in addition to time-domain slicing. We propose a simple and effective out-of-core scheme, where we integrate our PTOT with implicit occluders, batched occlusion queries and batched CUDA computing tasks, so that we can greatly reduce the I/O cost as well as increase the amount of data being concurrently computed in GPU.This results in an efficient algorithm for isosurface extraction with view-dependent filtering utilizing a state-of-the-art programmable GPU for time-varying fields larger than main memory. Our experiments on datasets as large as 192 GB (with 4 GB per time step) having no more than 870 MB of memory footprint in both preprocessing and run-time phases demonstrate the efficacy of our new technique.
Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs
Authors: David Meirion Hughes, Ik Soo Lim
Published at Vis, 2009

Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.
Loop surgery for volumetric meshes: Reeb graphs reduced to contour trees
Authors: Julien Tierny, Attila Gyulassy, Eddie Simon, Valerio Pascucci
Published at Vis, 2009

This paper introduces an efficient algorithm for computing the Reeb graph of a scalar function f defined on a volumetric mesh M in Ropf&lt;sup&gt;3&lt;/sup&gt;. We introduce a procedure called "loop surgery" that transforms M into a mesh M' by a sequence of cuts and guarantees the Reeb graph of f(M') to be loop free. Therefore, loop surgery reduces Reeb graph computation to the simpler problem of computing a contour tree, for which well-known algorithms exist that are theoretically efficient (O(n log n)) and fast in practice. Inverse cuts reconstruct the loops removed at the beginning. The time complexity of our algorithm is that of a contour tree computation plus a loop surgery overhead, which depends on the number of handles of the mesh. Our systematic experiments confirm that for real-life data, this overhead is comparable to the computation of the contour tree, demonstrating virtually linear scalability on meshes ranging from 70 thousand to 3.5 million tetrahedra. Performance numbers show that our algorithm, although restricted to volumetric data, has an average speedup factor of 6,500 over the previous fastest techniques, handling larger and more complex data-sets. We demonstrate the verstility of our approach by extending fast topologically clean isosurface extraction to non simply-connected domains. We apply this technique in the context of pressure analysis for mechanical design. In this case, our technique produces results in matter of seconds even for the largest meshes. For the same models, previous Reeb graph techniques do not produce a result.
Mapping High-fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures
Authors: Mikhail Smelyanskiy, David R. Holmes 0001, Jatin Chhugani, Alan Larson, Doug Carmean, Dennis P. Hanson, Pradeep Dubey, Kurt Augustine, Daehyun Kim 0001, Alan Kyker, Victor W. Lee, Anthony D. Nguyen, Larry Seiler, Richard A. Robb
Published at Vis, 2009

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel&lt;sup&gt;reg&lt;/sup&gt; architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.
Markerless View-Independent Registration of Multiple Distorted Projectors on Extruded Surfaces Using an Uncalibrated Camera
Authors: Behzad Sajadi, Aditi Majumder
Published at Vis, 2009

In this paper, we present the first algorithm to geometrically register multiple projectors in a view-independent manner (i.e. wallpapered) on a common type of curved surface, vertically extruded surface, using an uncalibrated camera without attaching any obtrusive markers to the display screen. Further, it can also tolerate large non-linear geometric distortions in the projectors as is common when mounting short throw lenses to allow a compact set-up. Our registration achieves sub-pixel accuracy on a large number of different vertically extruded surfaces and the image correction to achieve this registration can be run in real time on the GPU. This simple markerless registration has the potential to have a large impact on easy set-up and maintenance of large curved multi-projector displays, common for visualization, edutainment, training and simulation applications.
Multi-Scale Surface Descriptors
Authors: Gregory Cipriano, George N. Phillips Jr., Michael Gleicher
Published at Vis, 2009

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis.
Multimodal Vessel Visualization of Mouse Aorta PET/CT Scans
Authors: Timo Ropinski, Sven Hermann, Rainer Reich, Michael Schäfers 0001, Klaus H. Hinrichs
Published at Vis, 2009

In this paper, we present a visualization system for the visual analysis of PET/CT scans of aortic arches of mice. The system has been designed in close collaboration between researchers from the areas of visualization and molecular imaging with the objective to get deeper insights into the structural and molecular processes which take place during plaque development. Understanding the development of plaques might lead to a better and earlier diagnosis of cardiovascular diseases, which are still the main cause of death in the western world. After motivating our approach, we will briefly describe the multimodal data acquisition process before explaining the visualization techniques used. The main goal is to develop a system which supports visual comparison of the data of different species. Therefore, we have chosen a linked multi-view approach, which amongst others integrates a specialized straightened multipath curved planar reformation and a multimodal vessel flattening technique. We have applied the visualization concepts to multiple data sets, and we will present the results of this investigation.
Parameter Sensitivity Visualization for DTI fiber Tracking
Authors: Ralph Brecheisen, Anna Vilanova, Bram Platel, Bart M. ter Haar Romeny
Published at Vis, 2009

Fiber tracking of diffusion tensor imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very different results. The user remains completely unaware of such effects. Furthermore, it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique.
Perception-Based Transparency Optimization for Direct Volume Rendering
Authors: Ming-Yuen Chan, Yingcai Wu, Wai-Ho Mak, Wei Chen 0001, Huamin Qu
Published at Vis, 2009

The semi-transparent nature of direct volume rendered images is useful to depict layered structures in a volume. However, obtaining a semi-transparent result with the layers clearly revealed is difficult and may involve tedious adjustment on opacity and other rendering parameters. Furthermore, the visual quality of layers also depends on various perceptual factors. In this paper, we propose an auto-correction method for enhancing the perceived quality of the semi-transparent layers in direct volume rendered images. We introduce a suite of new measures based on psychological principles to evaluate the perceptual quality of transparent structures in the rendered images. By optimizing rendering parameters within an adaptive and intuitive user interaction process, the quality of the images is enhanced such that specific user requirements can be met. Experimental results on various datasets demonstrate the effectiveness and robustness of our method.
Predictor-Corrector Schemes for Visualization ofSmoothed Particle Hydrodynamics Data
Authors: Benjamin Schindler, Raphael Fuchs, John Biddiscombe, Ronald Peikert
Published at Vis, 2009

In this paper we present a method for vortex core line extraction which operates directly on the smoothed particle hydrodynamics (SPH) representation and, by this, generates smoother and more (spatially and temporally) coherent results in an efficient way. The underlying predictor-corrector scheme is general enough to be applied to other line-type features and it is extendable to the extraction of surfaces such as isosurfaces or Lagrangian coherent structures. The proposed method exploits temporal coherence to speed up computation for subsequent time steps. We show how the predictor-corrector formulation can be specialized for several variants of vortex core line definitions including two recent unsteady extensions, and we contribute a theoretical and practical comparison of these. In particular, we reveal a close relation between unsteady extensions of Fuchs et al. and Weinkauf et al. and we give a proof of the Galilean invariance of the latter. When visualizing SPH data, there is the possibility to use the same interpolation method for visualization as has been used for the simulation. This is different from the case of finite volume simulation results, where it is not possible to recover from the results the spatial interpolation that was used during the simulation. Such data are typically interpolated using the basic trilinear interpolant, and if smoothness is required, some artificial processing is added. In SPH data, however, the smoothing kernels are specified from the simulation, and they provide an exact and smooth interpolation of data or gradients at arbitrary points in the domain.
Quantitative Texton Sequences for Legible Bivariate Maps
Authors: Colin Ware
Published at Vis, 2009

Representing bivariate scalar maps is a common but difficult visualization problem. One solution has been to use two dimensional color schemes, but the results are often hard to interpret and inaccurately read. An alternative is to use a color sequence for one variable and a texture sequence for another. This has been used, for example, in geology, but much less studied than the two dimensional color scheme, although theory suggests that it should lead to easier perceptual separation of information relating to the two variables. To make a texture sequence more clearly readable the concept of the quantitative texton sequence (QTonS) is introduced. A QTonS is defined a sequence of small graphical elements, called textons, where each texton represents a different numerical value and sets of textons can be densely displayed to produce visually differentiable textures. An experiment was carried out to compare two bivariate color coding schemes with two schemes using QTonS for one bivariate map component and a color sequence for the other. Two different key designs were investigated (a key being a sequence of colors or textures used in obtaining quantitative values from a map). The first design used two separate keys, one for each dimension, in order to measure how accurately subjects could independently estimate the underlying scalar variables. The second key design was two dimensional and intended to measure the overall integral accuracy that could be obtained. The results show that the accuracy is substantially higher for the QTonS/color sequence schemes. A hypothesis that texture/color sequence combinations are better for independent judgments of mapped quantities was supported. A second experiment probed the limits of spatial resolution for QTonSs.
Sampling and Visualizing Creases with Scale-Space Particles
Authors: Gordon L. Kindlmann, Raúl San José Estépar, Stephen M. Smith 0001, Carl-Fredrik Westin
Published at Vis, 2009

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n + 1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI.
Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets
Authors: Won-Ki Jeong, Johanna Beyer, Markus Hadwiger, Amelio Vázquez Reina, Hanspeter Pfister, Ross T. Whitaker
Published at Vis, 2009

Recent advances in scanning technology provide high resolution EM (electron microscopy) datasets that allow neuro-scientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.
Stress Tensor field Visualization for Implant Planning in Orthopedics
Authors: Christian Dick, Joachim Georgii, Rainer Burgkart, Rüdiger Westermann
Published at Vis, 2009

We demonstrate the application of advanced 3D visualization techniques to determine the optimal implant design and position in hip joint replacement planning. Our methods take as input the physiological stress distribution inside a patient's bone under load and the stress distribution inside this bone under the same load after a simulated replacement surgery. The visualization aims at showing principal stress directions and magnitudes, as well as differences in both distributions. By visualizing changes of normal and shear stresses with respect to the principal stress directions of the physiological state, a comparative analysis of the physiological stress distribution and the stress distribution with implant is provided, and the implant parameters that most closely replicate the physiological stress state in order to avoid stress shielding can be determined. Our method combines volume rendering for the visualization of stress magnitudes with the tracing of short line segments for the visualization of stress directions. To improve depth perception, transparent, shaded, and antialiased lines are rendered in correct visibility order, and they are attenuated by the volume rendering. We use a focus+context approach to visually guide the user to relevant regions in the data, and to support a detailed stress analysis in these regions while preserving spatial context information. Since all of our techniques have been realized on the GPU, they can immediately react to changes in the simulated stress tensor field and thus provide an effective means for optimal implant selection and positioning in a computational steering environment.
Structuring Feature Space: A Non-Parametric Method for Volumetric Transfer Function Generation
Authors: Ross Maciejewski, Insoo Woo, Wei Chen 0001, David S. Ebert
Published at Vis, 2009

The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial transfer function generation serves as a reasonable base for volumetric rendering, reducing the trial-and-error overhead typically found in transfer function design.