DevLan/vispubs huggingface dataset:
Low-Pass Filtered Volumetric Shadows
Authors: Marco Ament, Filip Sadlo, Carsten Dachsbacher, Daniel Weiskopf
Published at SciVis, 2014

We present a novel and efficient method to compute volumetric soft shadows for interactive direct volume visualization to improve the perception of spatial depth. By direct control of the softness of volumetric shadows, disturbing visual patterns due to hard shadows can be avoided and users can adapt the illumination to their personal and application-specific requirements. We compute the shadowing of a point in the data set by employing spatial filtering of the optical depth over a finite area patch pointing toward each light source. Conceptually, the area patch spans a volumetric region that is sampled with shadow rays; afterward, the resulting optical depth values are convolved with a low-pass filter on the patch. In the numerical computation, however, to avoid expensive shadow ray marching, we show how to align and set up summed area tables for both directional and point light sources. Once computed, the summed area tables enable efficient evaluation of soft shadows for each point in constant time without shadow ray marching and the softness of the shadows can be controlled interactively. We integrated our method in a GPU-based volume renderer with ray casting from the camera, which offers interactive control of the transfer function, light source positions, and viewpoint, for both static and time-dependent data sets. Our results demonstrate the benefit of soft shadows for visualization to achieve user-controlled illumination with many-point lighting setups for improved perception combined with high rendering speed.
Multi-Charts for Comparative 3D Ensemble Visualization
Authors: Ismail Demir, Christian Dick, Rüdiger Westermann
Published at SciVis, 2014

A comparative visualization of multiple volume data sets is challenging due to the inherent occlusion effects, yet it is important to effectively reveal uncertainties, correlations and reliable trends in 3D ensemble fields. In this paper we present bidirectional linking of multi-charts and volume visualization as a means to analyze visually 3D scalar ensemble fields at the data level. Multi-charts are an extension of conventional bar and line charts: They linearize the 3D data points along a space-filling curve and draw them as multiple charts in the same plot area. The bar charts encode statistical information on ensemble members, such as histograms and probability densities, and line charts are overlayed to allow comparing members against the ensemble. Alternative linearizations based on histogram similarities or ensemble variation allow clustering of spatial locations depending on data distribution. Multi-charts organize the data at multiple scales to quickly provide overviews and enable users to select regions exhibiting interesting behavior interactively. They are further put into a spatial context by allowing the user to brush or query value intervals and specific distributions, and to simultaneously visualize the corresponding spatial points via volume rendering. By providing a picking mechanism in 3D and instantly highlighting the corresponding data points in the chart, the user can go back and forth between the abstract and the 3D view to focus the analysis.
Multiscale Symmetry Detection in Scalar Fields by Clustering Contours
Authors: Dilip Mathew Thomas, Vijay Natarajan
Published at SciVis, 2014

The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a high-dimensional transformation-invariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach.
Predicate-Based Focus-and-Context Visualization for 3D Ultrasound
Authors: Christian Schulte zu Berge, Maximilian Baust, Ankur Kapoor, Nassir Navab
Published at SciVis, 2014

Direct volume visualization techniques offer powerful insight into volumetric medical images and are part of the clinical routine for many applications. Up to now, however, their use is mostly limited to tomographic imaging modalities such as CT or MRI. With very few exceptions, such as fetal ultrasound, classic volume rendering using one-dimensional intensity-based transfer functions fails to yield satisfying results in case of ultrasound volumes. This is particularly due its gradient-like nature, a high amount of noise and speckle, and the fact that individual tissue types are rather characterized by a similar texture than by similar intensity values. Therefore, clinicians still prefer to look at 2D slices extracted from the ultrasound volume. In this work, we present an entirely novel approach to the classification and compositing stage of the volume rendering pipeline, specifically designed for use with ultrasonic images. We introduce point predicates as a generic formulation for integrating the evaluation of not only low-level information like local intensity or gradient, but also of high-level information, such as non-local image features or even anatomical models. Thus, we can successfully filter clinically relevant from non-relevant information. In order to effectively reduce the potentially high dimensionality of the predicate configuration space, we propose the predicate histogram as an intuitive user interface. This is augmented by a scribble technique to provide a comfortable metaphor for selecting predicates of interest. Assigning importance factors to the predicates allows for focus-and-context visualization that ensures to always show important (focus) regions of the data while maintaining as much context information as possible. Our method naturally integrates into standard ray casting algorithms and yields superior results in comparison to traditional methods in terms of visualizing a specific target anatomy in ultrasound volumes.
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Authors: Ronell Sicat, Jens H. Krüger, Torsten Möller, Markus Hadwiger
Published at SciVis, 2014

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.
Stent Maps - Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations
Authors: Silvia Born, Simon H. Sündermann, Christoph Russ, Raoul Hopf, Carlos E. Ruiz, Volkmar Falk, Michael Gessat
Published at SciVis, 2014

Transcatheter aortic valve implantation (TAVI) is a minimally-invasive method for the treatment of aortic valve stenosis in patients with high surgical risk. Despite the success of TAVI, side effects such as paravalvular leakages can occur postoperatively. The goal of this project is to quantitatively analyze the co-occurrence of this complication and several potential risk factors such as stent shape after implantation, implantation height, amount and distribution of calcifications, and contact forces between stent and surrounding structure. In this paper, we present a two-dimensional visualization (stent maps), which allows (1) to comprehensively display all these aspects from CT data and mechanical simulation results and (2) to compare different datasets to identify patterns that are typical for adverse effects. The area of a stent map represents the surface area of the implanted stent - virtually straightened and uncoiled. Several properties of interest, like radial forces or stent compression, are displayed in this stent map in a heatmap-like fashion. Important anatomical landmarks and calcifications are plotted to show their spatial relation to the stent and possible correlations with the color-coded parameters. To provide comparability, the maps of different patient datasets are spatially adjusted according to a corresponding anatomical landmark. Also, stent maps summarizing the characteristics of different populations (e.g. with or without side effects) can be generated. Up to this point several interesting patterns have been observed with our technique, which remained hidden when examining the raw CT data or 3D visualizations of the same data. One example are obvious radial force maxima between the right and non-coronary valve leaflet occurring mainly in cases without leakages. These observations confirm the usefulness of our approach and give starting points for new hypotheses and further analyses. Because of its reduced dimensionality, the stent map data is an appropriate input for statistical group evaluation and machine learning methods.
Trajectory-Based Flow Feature Tracking in Joint Particle/Volume Datasets
Authors: Franz Sauer, Hongfeng Yu 0001, Kwan-Liu Ma
Published at SciVis, 2014

Studying the dynamic evolution of time-varying volumetric data is essential in countless scientific endeavors. The ability to isolate and track features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. In addition, this paper presents a few other applications of this approach, such as the ability to efficiently track the internal properties of volumetric features using variables from the particle data. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy.
Trend-Centric Motion Visualization: Designing and Applying a New Strategy for Analyzing Scientific Motion Collections
Authors: David Schroeder, Fedor Korsakov, Carissa Mai-Ping Knipe, Lauren Thorson, Arin M. Ellingson, David J. Nuckley, John V. Carlis, Daniel F. Keefe
Published at SciVis, 2014

In biomechanics studies, researchers collect, via experiments or simulations, datasets with hundreds or thousands of trials, each describing the same type of motion (e.g., a neck flexion-extension exercise) but under different conditions (e.g., different patients, different disease states, pre- and post-treatment). Analyzing similarities and differences across all of the trials in these collections is a major challenge. Visualizing a single trial at a time does not work, and the typical alternative of juxtaposing multiple trials in a single visual display leads to complex, difficult-to-interpret visualizations. We address this problem via a new strategy that organizes the analysis around motion trends rather than trials. This new strategy matches the cognitive approach that scientists would like to take when analyzing motion collections. We introduce several technical innovations making trend-centric motion visualization possible. First, an algorithm detects a motion collection's trends via time-dependent clustering. Second, a 2D graphical technique visualizes how trials leave and join trends. Third, a 3D graphical technique, using a median 3D motion plus a visual variance indicator, visualizes the biomechanics of the set of trials within each trend. These innovations are combined to create an interactive exploratory visualization tool, which we designed through an iterative process in collaboration with both domain scientists and a traditionally-trained graphic designer. We report on insights generated during this design process and demonstrate the tool's effectiveness via a validation study with synthetic data and feedback from expert musculoskeletal biomechanics researchers who used the tool to analyze the effects of disc degeneration on human spinal kinematics.
Using Topological Analysis to Support Event-Guided Exploration in Urban Data
Authors: Harish Doraiswamy, Nivan Ferreira, Theodoros Damoulas, Juliana Freire, Cláudio T. Silva
Published at SciVis, 2014

The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies.
ViSlang: A System for Interpreted Domain-Specific Languages for Scientific Visualization
Authors: Peter Rautek, Stefan Bruckner, M. Eduard Gröller, Markus Hadwiger
Published at SciVis, 2014

Researchers from many domains use scientific visualization in their daily practice. Existing implementations of algorithms usually come with a graphical user interface (high-level interface), or as software library or source code (low-level interface). In this paper we present a system that integrates domain-specific languages (DSLs) and facilitates the creation of new DSLs. DSLs provide an effective interface for domain scientists avoiding the difficulties involved with low-level interfaces and at the same time offering more flexibility than high-level interfaces. We describe the design and implementation of ViSlang, an interpreted language specifically tailored for scientific visualization. A major contribution of our design is the extensibility of the ViSlang language. Novel DSLs that are tailored to the problems of the domain can be created and integrated into ViSlang. We show that our approach can be added to existing user interfaces to increase the flexibility for expert users on demand, but at the same time does not interfere with the user experience of novice users. To demonstrate the flexibility of our approach we present new DSLs for volume processing, querying and visualization. We report the implementation effort for new DSLs and compare our approach with Matlab and Python implementations in terms of run-time performance.
Visualization of Brain Microstructure Through Spherical Harmonics Illumination of High Fidelity Spatio-Angular Fields
Authors: Sujal Bista, Jiachen Zhuo, Rao P. Gullapalli, Amitabh Varshney
Published at SciVis, 2014

Diffusion kurtosis imaging (DKI) is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared to traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of the neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injury where DTI is often found to be inadequate. The DKI dataset, which has high-fidelity spatio-angular fields, is difficult to visualize. Glyph-based visualization techniques are commonly used for visualization of DTI datasets; however, due to the rapid changes in orientation, lighting, and occlusion, visually analyzing the much more higher fidelity DKI data is a challenge. In this paper, we provide a systematic way to manage, analyze, and visualize high-fidelity spatio-angular fields from DKI datasets, by using spherical harmonics lighting functions to facilitate insights into the brain microstructure.
Visualization of Regular Maps: The Chase Continues
Authors: Jarke J. van Wijk
Published at SciVis, 2014

A regular map is a symmetric tiling of a closed surface, in the sense that all faces, vertices, and edges are topologically indistinguishable. Platonic solids are prime examples, but also for surfaces with higher genus such regular maps exist. We present a new method to visualize regular maps. Space models are produced by matching regular maps with target shapes in the hyperbolic plane. The approach is an extension of our earlier work. Here a wider variety of target shapes is considered, obtained by duplicating spherical and toroidal regular maps, merging triangles, punching holes, and gluing the edges. The method produces about 45 new examples, including the genus 7 Hurwitz surface.
Visualizing 2-dimensional Manifolds with Curve Handles in 4D
Authors: Hui Zhang 0006, Jianguang Weng, Guangchen Ruan
Published at SciVis, 2014

In this paper, we present a mathematical visualization paradigm for exploring curves embedded in 3D and surfaces in 4D mathematical world. The basic problem is that, 3D figures of 4D mathematical entities often twist, turn, and fold back on themselves, leaving important properties behind the surface sheets. We propose an interactive system to visualize the topological features of the original 4D surface by slicing its 3D figure into a series of feature diagram. A novel 4D visualization interface is designed to allow users to control 4D topological shapes via the collection of diagram handles using the established curve manipulation mechanism. Our system can support rich mathematical interaction of 4D mathematical objects which is very difficult with any existing approach. We further demonstrate the effectiveness of the proposed visualization tool using various experimental results and cases studies.
Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems
Authors: Hyungsuk Choi, Woohyuk Choi, Tran Minh Quan, David G. C. Hildebrand, Hanspeter Pfister, Won-Ki Jeong
Published at SciVis, 2014

As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and GPU accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing GPU clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation.
Volume-Preserving Mapping and Registration for Collective Data Visualization
Authors: Jiaxi Hu, Guangyu Zou, Jing Hua 0001
Published at SciVis, 2014

In order to visualize and analyze complex collective data, complicated geometric structure of each data is desired to be mapped onto a canonical domain to enable map-based visual exploration. This paper proposes a novel volume-preserving mapping and registration method which facilitates effective collective data visualization. Given two 3-manifolds with the same topology, there exists a mapping between them to preserve each local volume element. Starting from an initial mapping, a volume restoring diffeomorphic flow is constructed as a compressible flow based on the volume forms at the manifold. Such a flow yields equality of each local volume element between the original manifold and the target at its final state. Furthermore, the salient features can be used to register the manifold to a reference template by an incompressible flow guided by a divergence-free vector field within the manifold. The process can retain the equality of local volume elements while registering the manifold to a template at the same time. An efficient and practical algorithm is also presented to generate a volume-preserving mapping and a salient feature registration on discrete 3D volumes which are represented with tetrahedral meshes embedded in 3D space. This method can be applied to comparative analysis and visualization of volumetric medical imaging data across subjects. We demonstrate an example application in multimodal neuroimaging data analysis and collective data visualization.