Bonneau / Ertl / Nielson Scientific Visualization: The Visual Extraction of Knowledge from Data

1. Auflage 2006
ISBN: 978-3-540-30790-7
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 434 Seiten

One of the greatest scientific challenges of the 21st century is how to master, organize and extract useful knowledge from the overwhelming flow of information made available by today's data acquisition systems and computing resources. Visualization is the premium means of taking up this challenge. This book is based on selected lectures given by leading experts in scientific visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The book contains more than 350 color illustrations.

Autoren/Hrsg.

Bonneau, Georges-Pierre

Ertl, Thomas

Nielson, Gregory

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Part I Meshes for Visualization;11
3.1;Adaptive Contouring with Quadratic Tetrahedra;13
3.1.1;1 Introduction;13
3.1.2;2 Previous Work;14
3.1.3;3 Quadratic Tetrahedra;15
3.1.4;4 Constructing a Quadratic Representation;16
3.1.5;5 Contouring Quadratic Tetrahedra;18
3.1.6;6 Results;19
3.1.7;7 Conclusions;23
3.1.8;Acknowledgments;23
3.1.9;References;24
3.2;On the Convexification of Unstructured Grids from a Scientific Visualization Perspective;27
3.2.1;1 Introduction;27
3.2.2;2 Williams’ Convexi.cation Framework;31
3.2.3;3 Delaunay-Based Techniques;33
3.2.4;4 Direct Convexification Approaches Using BSP-trees;35
3.2.5;5 Final Remarks;41
3.2.6;Acknowledgements;42
3.2.7;References;42
3.3;Brain Mapping Using Topology Graphs Obtained by Surface Segmentation;45
3.3.1;1 Introduction;45
3.3.2;2 Related Work;46
3.3.3;3 Brain Mapping;47
3.3.4;4 Isosurface Extraction;49
3.3.5;5 Multiresolution Surface Representation;49
3.3.6;6 Surface Segmentation;51
3.3.7;7 Topology Graph;54
3.3.8;8 Graph Mapping;55
3.3.9;9 Conclusions and FutureWork;56
3.3.10;Acknowledgments;56
3.3.11;References;57
3.4;Computing and Displaying Intermolecular Negative Volume for Docking;59
3.4.1;1 Introduction;59
3.4.2;2 Previous Work;60
3.4.3;3 Defining the Intermolecular Negative Volume;63
3.4.4;4 Computing the Intermolecular Negative Volume;65
3.4.5;5 Visualizing the Intermolecular Negative Volume;66
3.4.6;6 Modi.cations for More Accurate Computation;68
3.4.7;7 Interactive Visualization;69
3.4.8;8 Conclusions and FutureWork;72
3.4.9;Acknowledgements;72
3.4.10;References;73
3.5;Optimized Bounding Polyhedra for GPU-Based Distance Transform;75
3.5.1;1 Introduction;75
3.5.2;2 Related Work;76
3.5.3;3 Distance Field Methods for Triangle Meshes;78
3.5.4;4 Results;82
3.5.5;5 Conclusion;84
3.5.6;Acknowledgment;84
3.5.7;References;84
3.5.8;Appendix: Proof for Complete Covering Around Convex/ Concave Vertices;86
3.6;Generating, Representing and Querying Level-Of-Detail Tetrahedral Meshes;89
3.6.1;1 Introduction;89
3.6.2;2 Background;90
3.6.3;3 LOD Models;91
3.6.4;4 Generating LOD Models;95
3.6.5;5 Data Structures for Unstructured LOD Models;96
3.6.6;6 Encoding and Querying Nested LOD Models;99
3.6.7;7 Concluding Remarks;102
3.6.8;Acknowledgments;103
3.6.9;References;104
3.7;Split ’N Fit: Adaptive Fitting of Scattered Point Cloud Data;107
3.7.1;1 Problem and Basic Strategy;107
3.7.2;2 Best Approximation and Characterization;109
3.7.3;3 Re.nement Strategy;113
3.7.4;4 Examples;115
3.7.5;Acknowledgments;121
3.7.6;References;121
4;Part II Volume Visualization and Medical Visualization;123
4.1;Ray Casting with Programmable Graphics Hardware;125
4.1.1;1 Introduction;125
4.1.2;2 Ray Casting for Volume Visualization;126
4.1.3;3 Ray Casting Based on Programmable Graphics Hardware;132
4.1.4;4 Optimization Techniques;134
4.1.5;5 Conclusions;139
4.1.6;References;139
4.2;Volume Exploration Made Easy Using Feature Maps;141
4.2.1;1 Introduction;141
4.2.2;2 Preliminaries and Related Work;143
4.2.3;3 Density Range Migration;146
4.2.4;4 Volume Exploration with Feature Maps;154
4.2.5;5 Conclusions;156
4.2.6;Acknowledgements;157
4.2.7;References;157
4.3;Fantastic Voyage of the Virtual Colon;159
4.3.1;1 Introduction;159
4.3.2;2 Segmentation and Electronic Cleansing;160
4.3.3;3 Colon Centerline;163
4.3.4;4 Virtual Navigation;164
4.3.5;5 Interactive Volume Rendering;167
4.3.6;6 Clinical Results;169
4.3.7;Acknowledgements;170
4.3.8;References;170
4.4;Volume Denoising for Visualizing Refraction;173
4.4.1;1 Introduction;173
4.4.2;2 Related Work;176
4.4.3;3 Motivation;177
4.4.4;4 Denoising Methods;177
4.4.5;5 Metrics for Measuring Distortion and Smoothing Effects;183
4.4.6;6 Results and Remarks;187
4.4.7;7 Conclusions and FutureWork;190
4.4.8;Acknowledgments;192
4.4.9;References;193
4.5;Emphasizing Isosurface Embeddings in Direct Volume Rendering;195
4.5.1;1 Introduction;195
4.5.2;2 Related Work;196
4.5.3;3 Extracting Isosurface Embeddings;198
4.5.4;4 Emphasizing Isosurface Embeddings;206
4.5.5;5 Application to Real Datasets;211
4.5.6;6 Discussion;213
4.5.7;7 Conclusion;213
4.5.8;Acknowledgements;214
4.5.9;References;214
4.6;Diagnostic Relevant Visualization of Vascular Structures;217
4.6.1;1 Introduction;217
4.6.2;2 Related Work;218
4.6.3;3 Single Vessel CPR Methods;219
4.6.4;4 Vessel Tree CPR Methods;224
4.6.5;5 Results;231
4.6.6;6 Conclusions;235
4.6.7;Acknowledgements;237
4.6.8;References;237
5;Part III Vector Field Visualization;239
5.1;Clifford Convolution and Pattern Matching on Irregular Grids;241
5.1.1;1 Introduction and Related Work;241
5.1.2;2 Related Work;242
5.1.3;3 Clifford Algebra;245
5.1.4;4 Clifford Convolution;247
5.1.5;5 Convolution on Irregular Grids;251
5.1.6;6 Results;254
5.1.7;7 Conclusion and FutureWork;257
5.1.8;Acknowledgments;257
5.1.9;References;257
5.2;Fast and Robust Extraction of Separation Line Features;259
5.2.1;1 Introduction;259
5.2.2;2 Related Work;261
5.2.3;3 Wall Streamlines over a Simplicial Surface;262
5.2.4;4 Local Predictor;266
5.2.5;5 Ridge and Valley Lines Extraction;266
5.2.6;6 Accumulation Monitoring;267
5.2.7;7 Results;269
5.2.8;8 Conclusion;271
5.2.9;Acknowledgments;273
5.2.10;References;273
5.3;Fast Vortex Axis Calculation Using Vortex Features and Identi . cation Algorithms;275
5.3.1;1 Introduction;275
5.3.2;2 Analytical Vortices and Their Features;276
5.3.3;3 Regions Containing Vortices;280
5.3.4;4 The Problem of Defining a Vortex Axis;284
5.3.5;5 A Fast Vortex Axis Detection Combination Approach;286
5.3.6;6 Implementation;292
5.3.7;7 Conclusions;293
5.3.8;References;294
5.4;Topological Features in Vector Fields;297
5.4.1;1 Introduction;297
5.4.2;2 Related Works;298
5.4.3;3 Theory;300
5.4.4;4 Detection in Planar Flows;302
5.4.5;5 Detection of Features in 3-D Vector Fields;305
5.4.6;6 Results;307
5.4.7;7 Impending Challenges;309
5.4.8;8 Conclusion;310
5.4.9;Acknowledgments;310
5.4.10;References;310
6;Part IV Visualization Systems;313
6.1;Generalizing Focus+Context Visualization;315
6.1.1;1 Introduction;315
6.1.2;2 Focus+Context Visualization;316
6.1.3;3 Separating Focus from Context;317
6.1.4;4 Generalized Focus–Context Discrimination;318
6.1.5;5 Interaction;331
6.1.6;6 Summary and Conclusions;332
6.1.7;Acknowledgments;334
6.1.8;References;335
6.2;Rule-based Morphing Techniques for Interactive Clothing Catalogs;339
6.2.1;1 Introduction;339
6.2.2;2 Related Work;340
6.2.3;3 Rule-based Morphing;343
6.2.4;4 System Overview;347
6.2.5;5 Results;351
6.2.6;6 Conclusion and FutureWork;352
6.2.7;Acknowledgements;352
6.2.8;References;352
6.3;A Practical System for Constrained Interactive Walkthroughs of Arbitrarily Complex Scenes;355
6.3.1;1 Introduction;355
6.3.2;2 Related Work;357
6.3.3;3 Overview;358
6.3.4;4 Visibility Polyhedrons;358
6.3.5;5 Depth Meshes and Geometric Simplification;363
6.3.6;6 Data Management;364
6.3.7;7 Error Analysis;371
6.3.8;8 Results and Discussions;373
6.3.9;9 Conclusions and FutureWork;373
6.3.10;References;375
6.4;Component Based Visualisation of DIET Applications;377
6.4.1;1 Introduction;377
6.4.2;2 DIET Software Platform;378
6.4.3;3 Visualisation Platform;382
6.4.4;4 Example;391
6.4.5;5 Conclusion;392
6.4.6;Acknowledgements;393
6.4.7;References;393
6.5;Facilitating the Visual Analysis of Large-Scale Unsteady Computational Fluid Dynamics Simulations;395
6.5.1;1 Introduction;395
6.5.2;2 Relevant Issues;396
6.5.3;3 Feature Detection and Feature Tracking;398
6.5.4;4 A New Visualization Pipeline for Large-Scale CFD Simulations;401
6.5.5;5 Conclusion;403
6.5.6;Acknowledgements;403
6.5.7;References;403
6.6;Evolving Data.ow Visualization Environments to Grid Computing;405
6.6.1;1 Introduction;405
6.6.2;2 Visualization and Grid Computing;406
6.6.3;3 Dataflow Visualization Environments;407
6.6.4;4 An Environmental Crisis – Pollution Alert;409
6.6.5;5 Simulating the Dispersal of the Pollutant;410
6.6.6;6 Computational Steering in a Grid Environment – Reference Model;411
6.6.7;7 Computational Steering in a Grid Environment – Demonstrator;412
6.6.8;8 Collaborative Computational Steering – Reference Model and Demonstrator;413
6.6.9;9 Conclusions and FutureWork;415
6.6.10;References;416
6.7;Earthquake Visualization Using Large-scale Ground Motion and Structural Response Simulations;419
6.7.1;1 Introduction;419
6.7.2;2 Related Work;422
6.7.3;3 Tetrahedral Fusion with Quadric Error Metrics;427
6.7.4;4 Time-varying Tetrahedral Mesh Decimation;433
6.7.5;5 Results from Ground Motion and Structural Response Simulation;437
6.7.6;6 Conclusions;439
6.7.7;Acknowledgements;440
6.7.8;References;440
7;Author Index;443

Leseproben

Generating, Representing and Querying Level-Of-Detail Tetrahedral Meshes (p. 79-80)

Leila De Floriani1,2 and Emanuele Danovaro1
1 Department of Computer Science, University of Genova, Genova, Italy
2 Department of Computer Science, University of Maryland, College Park, MD, USA

Summary. In this paper, we survey techniques for building, encoding and querying Level-
Of-Detail (LOD) models of three-dimensional scalar fields based on a domain decomposition
into tetrahedral meshes. We focus on continuous LOD models, and we classify them into
unstructured (irregular) and regular nested LOD models depending on the mesh subdivision
pattern and on the distribution of the data points.Within each class, we review data structures,
construction algorithms, as well as techniques for extracting adaptively refined field representations
from an LOD model.

1 Introduction

Level-Of-Detail (LOD) models have been proposed to control and adapt the accuracy in the representation of large-size volume data sets. LOD models encode in a compact data structure the steps performed by a refinement process applied to a coarse representation of a scalar field, or by a decimation process applied to a full- resolution representation. A large numbers of simplified meshes can be extracted from an LOD model, in which the resolution (i.e., the density of the cells) of the simplified mesh may vary in different parts of the field domain, or in the proximity of interesting field values. The extraction of a simplified representation from an LOD model is called a selective refinement. The challenge in designing an LOD model is represented by the trade-off between the efficiency of the selective refinement algorithms and the storage cost of the representation.

This paper reviews techniques proposed in the literature for encoding, generating and performing selective refinement on an LOD model. We focus on so-called continuous LOD models, from which a virtually continuous simplified adaptive representations can be extracted. Discrete (non-continuous) LOD models consist of a (usually small) collection of representations at different LODs and only representations of the scalar field at uniform resolutions can be extracted from them [6]. The remainder of this paper is organized as follows. Section 2 introduces some background notions on tetrahedral meshes and discusses data structures for encoding them. Section 3 introduces the basic elements of an LOD model and the most common update operations through which an LOD model is generated. Section 4 reviews incremental refinement and coarsening techniques used to generate unstructured LOD models. Section 5 discusses data structures for encoding unstructured LOD models. Section 6 reviews techniques for encoding and querying nested LOD models. Section 7 presents some comparisons of performances of unstructured and nested LOD models in extracting adaptively-refined meshes, and discusses some open research issues.

2 Background

A volume data set S consists of a set V of points in the three-dimensional Euclidean space, and of one or several field values associated with the points of V. The points in V can be regularly spaced, i.e., they are the vertices of a regular, rectilinear grid, or irregularly spaced. In the former case, we will call S a structured, or a regular, data set, while, in the latter case, we will call it an unstructured, or an irregular data set. A tetrahedral mesh S is a connected set of tetrahedra such that the union of all tetrahedra in S covers a domain D in 3D space and any two distinct tetrahedra have disjoint interiors. A tetrahedral mesh Sis called a conforming mesh if the intersection of the boundaries of any two tetrahedra s1 and s2 of S, which have a non- empty intersection, consists of lower dimensional simplexes (vertices, edges, or triangles) that belong to the boundary of both s1 and s2. Conforming meshes have a wellde .ned combinatorial structure in which each tetrahedron is adjacent to exactly one other tetrahedron along each of its faces. This is important when a tetrahedral mesh is used as a decomposition of the domain of a volume data set.

We call nested meshes those meshes which are defined by the uniform subdivision of a tetrahedron into scaled copies of it. In particular, we will consider nested regular meshes, in which the vertices are a subset of the vertices of a regular grid. A mesh which is not nested is called irregular, or unstructured. A mesh is called stable if the tetrahedra forming it satisfy some measure of non-degeneracy. Measures commonly used in the .nite element literature are the circumradius- to-shortest-edge ratio (where the circumradius is the radius of the circumsphere of a tetrahedron), and the minimum solid angle associated with a tetrahedron [39].