M. Han, I. Wald, W. Usher, Q. Wu, F. Wang, V. Pascicci, C. D. Hansen, C. R. Johnson.
Ray Tracing Generalized Tube Primitives: Method and Applications, In Computer Graphics Forum, Vol. 38, No. 3, John Wiley & Sons Ltd., 2019.
We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.
A.V. P. Grosset, A. Knoll, C.D. Hansen.
Dynamically Scheduled Region-Based Image Compositing, In Eurographics Symposium on Parallel Graphics and Visualization, June, 2016.
Algorithms for sort-last parallel volume rendering on large distributed memory machines usually divide a dataset equally across all nodes for rendering. Depending on the features that a user wants to see in a dataset, all the nodes will rarely finish rendering at the same time. Existing compositing algorithms do not often take this into consideration, which can lead to significant delays when nodes that are compositing wait for other nodes that are still rendering. In this paper, we present an image compositing algorithm that uses spatial and temporal awareness to dynamically schedule the exchange of regions in an image and progressively composite images as they become available. Running on the Edison supercomputer at NERSC, we show that a scheduler-based algorithm with awareness of the spatial contribution from each rendering node can outperform traditional image compositing algorithms.
P. Ljung, J. Krüger, E. Gröller, M. Hadwiger, C. D. Hansen,, A. Ynnerman.
State of the Art in Transfer Functions for Direct Volume Rendering, In Computer Graphics Forum, Vol. 35, No. 3, Wiley-Blackwell, pp. 669--691. June, 2016.
A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.
M. Kim, C.D. Hansen.
Surface Flow Visualization using the Closest Point Embedding, In 2015 IEEE Pacific Visualization Symposium, April, 2015.
In this paper, we introduce a novel flow visualization technique for arbitrary surfaces. This new technique utilizes the closest point embedding to represent the surface, which allows for accurate particle advection on the surface as well as supports the unsteady flow line integral convolution (UFLIC) technique on the surface. This global approach is faster than previous parameterization techniques and prevents the visual artifacts associated with image-based approaches.
Keywords: vector field, flow visualization
M. Kim, C.D. Hansen.
GPU Surface Extraction with the Closest Point Embedding, In Proceedings of IS&T/SPIE Visualization and Data Analysis, 2015, February, 2015.
Isosurface extraction is a fundamental technique used for both surface reconstruction and mesh generation. One method to extract well-formed isosurfaces is a particle system; unfortunately, particle systems can be slow. In this paper, we introduce an enhanced parallel particle system that uses the closest point embedding as the surface representation to speedup the particle system for isosurface extraction. The closest point embedding is used in the Closest Point Method (CPM), a technique that uses a standard three dimensional numerical PDE solver on two dimensional embedded surfaces. To fully take advantage of the closest point embedding, it is coupled with a Barnes-Hut tree code on the GPU. This new technique produces well-formed, conformal unstructured triangular and tetrahedral meshes from labeled multi-material volume datasets. Further, this new parallel implementation of the particle system is faster than any known methods for conformal multi-material mesh extraction. The resulting speed-ups gained in this implementation can reduce the time from labeled data to mesh from hours to minutes and benefits users, such as bioengineers, who employ triangular and tetrahedral meshes.
Keywords: scalar field methods, GPGPU, curvature based, scientific visualization
G.P. Bonneau, H.C. Hege, C.R. Johnson, M.M. Oliveira, K. Potter, P. Rheingans, T. Schultz.
Overview and State-of-the-Art of Uncertainty Visualization, In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Edited by M. Chen and H. Hagen and C.D. Hansen and C.R. Johnson and A. Kauffman, Springer-Verlag, pp. 3--27. 2014.
The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.
C.D. Hansen, M. Chen, C.R. Johnson, A.E. Kaufman, H. Hagen (Eds.).
Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Mathematics and Visualization, Springer, 2014.
T. Hollt, A. Magdy, P. Zhan, G. Chen, G. Gopalakrishnan, I. Hoteit, C.D. Hansen, M. Hadwiger.
Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles, In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. PP, No. 99, pp. 1. 2014.
We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis. The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures. Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.
Keywords: Ensemble Visualization, Ocean Visualization, Ocean Forecast, Risk Estimation
Y. Wan, H. Otsuna, K. Kwan, C.D. Hansen.
Real-Time Dense Nucleus Selection from Confocal Data, In Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine, 2014.
Selecting structures from volume data using direct over-the-visualization interactions, such as a paint brush, is perhaps the most intuitive method in a variety of application scenarios. Unfortunately, it seems difficult to design a universal tool that is effective for all different structures in biology research. In [WOCH12b], an interactive technique was proposed for extracting neural structures from confocal microscopy data. It uses a dual-stroke paint brush to select desired structures directly from volume visualizations. However, the technique breaks down when it was applied to selecting densely packed structures with condensed shapes, such as nuclei from zebrafish eye development research. We collaborated with biologists studying zebrafish eye development and adapted the paint brush tool for real-time nucleus selection from volume data. The morphological diffusion algorithm used in the previous paint brush is restricted to gradient descending directions for improved nucleus boundary definition. Occluded seeds are removed using backward ray-casting. The adapted paint brush is then used in tracking cell movements in a time sequence dataset of a developing zebrafish eye.
L. Zhou, C.D. Hansen.
GuideME: Slice-guided Semiautomatic Multivariate Exploration of Volumes, In Computer Graphics Forum, Vol. 33, No. 3, Wiley-Blackwell, pp. 151--160. jun, 2014.
Multivariate volume visualization is important for many applications including petroleum exploration and medicine. State-of-the-art tools allow users to interactively explore volumes with multiple linked parameter-space views. However, interactions in the parameter space using trial-and-error may be unintuitive and time consuming. Furthermore, switching between different views may be distracting. In this paper, we propose GuideME: a novel slice-guided semiautomatic multivariate volume exploration approach. Specifically, the approach comprises four stages: attribute inspection, guided uncertainty-aware lasso creation, automated feature extraction and optional spatial fine tuning and visualization. Throughout the exploration process, the user does not need to interact with the parameter views at all and examples of complex real-world data demonstrate the usefulness, efficiency and ease-of-use of our method.
C. Brownlee, T. Ize, C.D. Hansen.
Image-parallel Ray Tracing using OpenGL Interception, In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV 2013), pp. 65--72. 2013.
CPU Ray tracing in scientific visualization has been shown to be an efficient rendering algorithm for large-scale polygonal data on distributed-memory systems by using custom integrations which modify the source code of existing visualization tools or by using OpenGL interception to run without source code modification to existing tools. Previous implementations in common visualization tools use existing data-parallel work distribution with sort-last compositing algorithms and exhibited sub-optimal performance scaling across multiple nodes due to the inefficiencies of data-parallel distributions of the scene geometry. This paper presents a solution which uses efficient ray tracing through OpenGL interception using an image-parallel work distribution implemented on top of the data-parallel distribution of the host program while supporting a paging system for access to non-resident data. Through a series of scaling studies, we show that using an image-parallel distribution often provides superior scaling performance which is more independent of the data distribution and view, while also supporting secondary rays for advanced rendering effects.
A. Grosset, M. Schott, G.-P. Bonneau, C.D. Hansen.
Evaluation of Depth of Field for Depth Perception in DVR, In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 81--88. 2013.
In this paper we present a user study on the use of Depth of Field for depth perception in Direct Volume Rendering. Direct Volume Rendering with Phong shading and perspective projection is used as the baseline. Depth of Field is then added to see its impact on the correct perception of ordinal depth. Accuracy and response time are used as the metrics to evaluate the usefulness of Depth of Field. The onsite user study has two parts: static and dynamic. Eye tracking is used to monitor the gaze of the subjects. From our results we see that though Depth of Field does not act as a proper depth cue in all conditions, it can be used to reinforce the perception of which feature is in front of the other. The best results (high accuracy & fast response time) for correct perception of ordinal depth occurs when the front feature (out of the two features users were to choose from) is in focus and perspective projection is used.
T. Höllt, A. Magdy, G. Chen, G. Gopalakrishnan, I. Hoteit, C.D. Hansen, M. Hadwiger.
Visual Analysis of Uncertainties in Ocean Forecasts for Planning and Operation of Off-Shore Structures, In Proceedings of 2013 IEEE Pacific Visualization Symposium (PacificVis), Note: Received Honerable Mention, pp. 185--192. 2013.
We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Our system enables the interactive planning of both the placement and operation of off-shore structures. We illustrate this using a real-world simulation of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by strong loop currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. Nowadays, these forecasts are based on multiple spatio-temporal simulations resulting in multidimensional, multivariate and multivalued data, so-called ensemble data. Changes in sea surface elevation are a good indicator for the movement of loop current eddies, and our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations.
Keywords: Uncertainty, Ensemble Simulation, Risk Estimate
T. McLoughlin, M.W. Jones, R.S. Laramee, R. Malki, I. Masters, C.D. Hansen.
Similarity Measures for Enhancing Interactive Streamline Seeding, In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 8, pp. 1342--1353. 2013.
PubMed ID: 23744264
Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.
M. Schott, T. Martin, A.V.P. Grosset, S.T. Smith, C.D. Hansen.
Ambient Occlusion Effects for Combined Volumes and Tubular Geometry, In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 6, Note: Selected as Spotlight paper for June 2013 issue, pp. 913--926. 2013.
This paper details a method for interactive direct volume rendering that computes ambient occlusion effects for visualizations that combine both volumetric and geometric primitives, specifically tube shaped geometric objects representing streamlines, magnetic field lines or DTI fiber tracts. The algorithm extends the recently presented Directional Occlusion Shading model to allow the rendering of those geometric shapes in combination with a context providing 3D volume, considering mutual occlusion between structures represented by a volume or geometry. Stream tube geometries are computed using an effective spline based interpolation and approximation scheme that avoids self intersection and maintains coherent orientation of the stream tube segments to avoid surface deforming twists. Furthermore, strategies to reduce the geometric and specular aliasing of the stream tubes are discussed.
Keywords: Volume rendering, ambient occlusion, stream tubes
Y. Wan, H. Otsuna, C.D. Hansen.
Synthetic Brainbows, In Computer Graphics Forum, Vol. 32, No. 3pt4, Wiley-Blackwell, pp. 471--480. jun, 2013.
Brainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow-like images. In addition, we incorporate ID shuffling and Monte-Carlo sampling into our technique, so that it can be applied to single-channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.
L. Zhou, C.D. Hansen.
Transfer Function Design based on User Selected Samples for Intuitive Multivariate Volume Exploration, In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 73--80. 2013.
Multivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets.
L. Zhou, C.D. Hansen.
Interactive rendering and efficient querying for large multivariate seismic volumes on consumer level PCs, In Proceedings of the 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 117--118. 2013.
We present a volume visualization method that allows interactive rendering and efficient querying of large multivariate seismic volume data on consumer level PCs. The volume rendering pipeline utilizes a virtual memory structure that supports out-of-core multivariate multi-resolution data and a GPU-based ray caster that allows interactive multivariate transfer function design. A Gaussian mixture model representation is precomputed and nearly interactive querying is achieved by testing the Gaussian functions against user defined transfer functions on the GPU in the runtime. Finally, the method has been tested on a multivariate 3D seismic dataset which is larger than the size of the main memory of the testing machine.
C. Brownlee, T. Fogal, C.D. Hansen.
GLuRay: Ray Tracing in Scientific Visualization Applications using OpenGL Interception, In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (2012), Edited by H. Childs and T. Kuhlen and F. Marton, pp. 41--50. 2012.
Ray tracing in scientific visualization allows for substantial gains in performance and rendering quality with large scale polygonal datasets compared to brute-force rasterization, however implementing new rendering architectures into existing tools is often costly and time consuming. This paper presents a library, GLuRay, which intercepts OpenGL calls from many common visualization applications and renders them with the CPU ray tracer Manta without modification to the underlying visualization tool. Rendering polygonal models such as isosurfaces can be done identically to an OpenGL implementation using provided material and camera properties or superior rendering can be achieved using enhanced settings such as dielectric materials or pinhole cameras with depth of field effects. Comparative benchmarks were conducted on the Texas Advanced Computing Center’s Longhorn cluster using the popular visualization packages ParaView, VisIt, Ensight, and VAPOR. Through the parallel ren- dering package ParaView, scaling up to 64 nodes is demonstrated. With our tests we show that using OpenGL interception to accelerate and enhance visualization programs provides a viable enhancement to existing tools with little overhead and no code modification while allowing for the creation of publication quality renderings using advanced effects and greatly improved large-scale software rendering performance within tools that scientists are currently using.
Keywords: kaust, scidac
C. Brownlee, J. Patchett, L.-T. Lo, D. DeMarle, C. Mitchell, J. Ahrens, C.D. Hansen.
A Study of Ray Tracing Large-scale Scientific Data in Parallel Visualization Applications, In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (2012), Edited by H. Childs and T. Kuhlen and F. Marton, pp. 51--60. 2012.
Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simulations produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algorithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The software ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.
Keywords: kaust, scidac