Visualization and data analysis最新文献_第3页

Display Systems for Visualization and Simulation in Virtual Environments 虚拟环境中可视化和仿真的显示系统

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-391

T. Wischgoll

引用次数: 8

Accelerating advection via approximate block exterior flow maps 通过近似块外部流图加速平流

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-397

R. Bleile, L. Sugiyama, C. Garth, H. Childs

{"title":"Accelerating advection via approximate block exterior flow maps","authors":"R. Bleile, L. Sugiyama, C. Garth, H. Childs","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-397","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-397","url":null,"abstract":"Flow visualization techniques involving extreme advection workloads are becoming increasingly popular. While these techniques often produce insightful images, the execution times to carry out the corresponding computations are lengthy. With this work, we introduce an alternative to traditional advection, which improves on performance at the cost of decreased accuracy. Our approach centers around block exterior flow maps (BEFMs), which can be used to accelerate flow computations by reducing redundant calculations. Our algorithm uses Lagrangian interpolation, but falls back to Eulerian advection whenever regions of high error are encountered. In our study, we demonstrate that the BEFM-based approach can lead to significant savings in time, with limited loss in accuracy. Introduction A myriad of scientific simulations, including those modeling fluid flow, astrophysics, fusion, thermal hydraulics, and others, model phenomena where constituents move through their volume. This movement is captured by a velocity field stored at every point on the mesh. Further, other vector fields, such as force fields for electricity, magnetism, and gravity, also govern movement and interaction. A wide range of flow visualization techniques are used to understand such vector fields. The large majority of these techniques rely on placing particles in the volume and analyzing the trajectories they follow. Traditionally, the particles are displaced through the volume using an advection step, i.e., solving an ordinary differential equation using a Runge-Kutta integrator. As computational power on modern desktops has increased, flow visualization algorithms have been empowered to consider designs that include more and more particles advecting for longer and longer periods. Techniques such as Line Integral Convolution and Finite-Time Lyapunov Exponents (FTLE) seed particles densely in a volume and examine where these particles end up. For these operations, and many others, only the ending position of the particle is needed, and not the details of the path the particle took to get there. Despite seemingly abundant computational power, some techniques have excessively long running times. For example, ocean modelers often study the FTLE within an ocean with both high seeding density and very long durations for the particles (years of simulation time) [2, 3]. As another example, fusion scientists are interested in FTLE computations inside a tokamak where particles travel for hundreds of rotations [1]. In both cases, FTLE calculations, even on supercomputers, can take tens of minutes. With this work, we consider an alternative to traditional Eulerian advection. The key observation that motivates the work is that, in conditions with dense seeding and long durations, particles will tread the same (or very similar) paths over and over. Where the current paradigm carries out the same computation over and over, we consider a new paradigm where a computation can be carried out a single","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"95 1","pages":"140-148"},"PeriodicalIF":0.0,"publicationDate":"2017-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88288459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Megacity: A Collaborative Virtual Reality Environment for Emergency Response, Training, and Decision Making 大城市:用于应急响应、培训和决策的协同虚拟现实环境

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-390

Sharad Sharma, Phillip Devreaux, D. Scribner, J. Grynovicki, P. Grazaitis

引用次数: 14

A Visual Evaluation Study of Graph Sampling Techniques 图采样技术的视觉评价研究

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-394

Fangyan Zhang, Song Zhang, P. C. Wong, Hugh R. Medal, L. Bian, J. Swan, T. Jankun-Kelly

引用次数: 6

Declarative Guide Creation 创建声明式指南

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-386

Joseph A. Cottam, A. Lumsdaine

引用次数: 0

Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data 人类动作捕捉数据的视觉交互半监督标记

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-387

J. Bernard, Eduard Dobermann, Anna Vögele, Björn Krüger, J. Kohlhammer, D. Fellner

{"title":"Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data","authors":"J. Bernard, Eduard Dobermann, Anna Vögele, Björn Krüger, J. Kohlhammer, D. Fellner","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-387","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-387","url":null,"abstract":"The characterization and abstraction of large multivariate time series data often poses challenges with respect to effectiveness or efficiency. Using the example of human motion capture data challenges exist in creating compact solutions that still reflect semantics and kinematics in a meaningful way. We present a visual-interactive approach for the semi-supervised labeling of human motion capture data. Users are enabled to assign labels to the data which can subsequently be used to represent the multivariate time series as sequences of motion classes. The approach combines multiple views supporting the user in the visualinteractive labeling process. Visual guidance concepts further ease the labeling process by propagating the results of supportive algorithmic models. The abstraction of motion capture data to sequences of event intervals allows overview and detail-on-demand visualizations even for large and heterogeneous data collections. The guided selection of candidate data for the extension and improvement of the labeling closes the feedback loop of the semisupervised workflow. We demonstrate the effectiveness and the efficiency of the approach in two usage scenarios, taking visualinteractive learning and human motion synthesis as examples.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"47 1","pages":"34-45"},"PeriodicalIF":0.0,"publicationDate":"2017-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85137103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

An interactive tool for Analyzing the Correlation, Uncertainty, and Clustering (ACUC) over ensembles in climate dataset 气候数据集相关、不确定性和聚类(ACUC)分析的交互式工具

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-384

N. Abedzadeh

引用次数: 0

Visual Interactive Creation and Validation of Text Clustering Workflows to Explore Document Collections 探索文档集合的文本聚类工作流的可视化交互创建和验证

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-388

T. Ruppert, Michael Staab, A. Bannach, Hendrik Lücke-Tieke, J. Bernard, Arjan Kuijper, J. Kohlhammer

引用次数: 11

Analysis Enhanced Particle-based Flow Visualization 分析增强的基于颗粒的流动可视化

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-385

Lieyu Shi, Lei Zhang, Wei-Ju Cao, Guoning Chen

{"title":"Analysis Enhanced Particle-based Flow Visualization","authors":"Lieyu Shi, Lei Zhang, Wei-Ju Cao, Guoning Chen","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-385","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-385","url":null,"abstract":"Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"94 4 1","pages":"12-21"},"PeriodicalIF":0.0,"publicationDate":"2017-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83339590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Ray Traced Volume Clipping Using Multi-Hit BVH Traversal 使用多命中BVH遍历的光线跟踪体裁剪

Visualization and data analysis Pub Date : 2017-01-29 DOI: 10.2352/ISSN.2470-1173.2017.1.VDA-392

Stefan Zellmann, M. Hoevels, U. Lang

{"title":"Ray Traced Volume Clipping Using Multi-Hit BVH Traversal","authors":"Stefan Zellmann, M. Hoevels, U. Lang","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-392","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-392","url":null,"abstract":"Clipping is an important operation in the context of direct volume rendering to gain an understanding of the inner structures of scientific datasets. Rendering systems often only support volume clipping with geometry types that can be described in a parametric form, or they employ costly multi-pass GPU approaches. We present a SIMD-friendly clipping algorithm for ray traced direct volume rendering that is compatible with arbitrary geometric surface primitives ranging from mere planes over quadric surfaces such as spheres to general triangle meshes. By using a generic programming approach, our algorithm is in general not even limited to triangle or quadric primitives. Ray tracing complex geometric objects with a high primitive count requires the use of acceleration data structures. Our algorithm is based on the multi-hit query for traversing bounding volume hierarchies with rays. We provide efficient CPU and GPU implementations and present performance results. Introduction Clipping for 3-D direct volume rendering (DVR) plays an important role in many scientific visualization contexts. Clipping with spheres and planes can be a helpful tool in interactive scenarios as they occur in virtual reality (VR) applications with tracking devices. In such cases, volumetric datasets can be interactively explored by navigating through the rendered volumetric region and using the clip geometry as a virtual shield. In medical imaging contexts, static clipping with a nonmoving geometry is important e.g. in cases where neurologists have identified 3-D regions of interest in a magnetic resonance imaging (MRI) dataset and desire to suppress rendering for all content but that inside of those regions. Engineering applications often rely on DVR to display scalar or higher-order fields obtained from a simulation. For visualizations of this type it may be helpful to perform sub-voxel accurate clipping with the bounding geometry that was used during the simulation, especially if the volume dataset is blended and then displayed together with the bounding geometry. Traditional DVR applications, especially if they are intended to be used in VR scenarios and if low latency is crucial, typically employ hardware accelerated texture-based rendering with rasterization and a planar proxy geometry. Using this traditional pipeline, sub-voxel accurate clipping is hard to achieve with geometry that exposes irregular concavities. When using ray casting for volume integration, clipping with an arbitrarily shaped geometry can however elegantly be described in terms of a simple two-pass algorithm: in the first pass, intersect each primary viewing ray with all opaque geometry that is set up for clipping and identify visible volume regions. In the second rendering pass, cast primary rays through the volume density and consider only those voxels that are not clipped. A ray tracing-based algorithm lends itself well to this approach because intersecting rays with 3-D geometry can be efficiently","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"160 1","pages":"89-98"},"PeriodicalIF":0.0,"publicationDate":"2017-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73597330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7