Scientific Visualization: The visual extraction of knowledge from data

428 Pages·2005·6.918 MB·English

by  Georges-Pierre Bonneau, Thomas Ertl, Gregory M. Nielson

Mathematics and Visualization
SeriesEditors
GeraldFarin
Hans-ChristianHege
DavidHoffman
ChristopherR.Johnson
KonradPolthier
MartinRumpf
Georges-Pierre Bonneau
Thomas Ertl
Gregory M. Nielson
Editors
Scientific Visualization:
The Visual Extraction of
Knowledge from Data
With 228 Figures
ABC
Georges-PierreBonneau GregoryM.Nielson
UniversiteGrenobleI DepartmentofComputerScienceandEngineering
Lab.LMC-IMAG IraA.FultonSchoolofEngineering
BP53,38041GrenobleCX9 ArizonaStateUniversity
France Tempe,AZ85287-8809
E-mail:[email protected] USA
E-mail:[email protected]
ThomasErtl
UniversityofStuttgart
VisualizationandInteractiveSystems
Institute(VIS)
Universitätßtraße38
70569Stuttgart
Germany
E-mail:[email protected]
LibraryofCongressControlNumber:2005932239
MathematicsSubjectClassification:68-XX,68Uxx,68U05,65-XX,65Dxx,65D18
ISBN-10 3-540-26066-8SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-26066-0SpringerBerlinHeidelbergNewYork
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Preface
ScientificVisualizationisconcernedwithtechniquesthatallowscientistsandengi-
neers to extract knowledge from the results of simulations and computations. Ad-
vancesinscientificcomputationareallowingmathematicalmodelsandsimulations
tobecomeincreasinglycomplexanddetailed.Thisresultsinacloserapproximation
torealitythusenhancingthepossibilityofacquiringnewknowledgeandunderstand-
ing.Tremendouslylargecollectionsofnumericalvalues,whichcontainagreatdeal
of information, are being produced and collected. The problem is to convey all of
thisinformationtothescientistsothateffectiveusecanbemadeofthehumancre-
ativeandanalyticcapabilities.Thisrequiresamethodofcommunicationwithahigh
bandwidthandaneffectiveinterface.Computergeneratedimagesandhumanvision
mediated by the principles of perceptual psychology are the means used in scien-
tific visualization to achieve this communication. The foundation material for the
techniquesofScientificVisualizationarederivedfrommanyareasincluding,forex-
ample, computer graphics, image processing, computer vision, perceptual psychol-
ogy, applied mathematics, computer aided design, signal processing and numerical
analysis.
This book is based on selected lectures given by leading experts in Scientific
VisualizationduringaworkshopheldatSchlossDagstuhl,Germany.Topicsinclude
user issues in visualization, large data visualization, unstructured mesh processing
forvisualization,volumetricvisualization,flowvisualization,medicalvisualization
andvisualizationsystems.ThemethodsofvisualizingdatadevelopedbyScientific
Visualizationresearcherspresentedinthisbookarehavingbroadimpactontheway
other scientists, engineers and practitioners are processing and understanding their
datafromsensors,simulationsandmathematicsmodels.
Wewouldliketoexpressourwarmestthankstotheauthorsandrefereesfortheir
hardwork.WewouldalsoliketothankFabienVivodtzevforhishelpinadminister-
ingthereviewingandeditingprocess.
Grenoble, Georges-PierreBonneau
January2005 ThomasErtl
GregoryM.Nielson
Contents
PartI MeshesforVisualization
AdaptiveContouringwithQuadraticTetrahedra
BenjaminF.Gregorski,DavidF.Wiley,HenryR.Childs,BerndHamann,
KennethI.Joy .................................................... 3
OntheConvexificationofUnstructuredGrids
fromaScientificVisualizationPerspective
Joa˜oL.D.Comba,JosephS.B.Mitchell,Cla´udioT.Silva ................. 17
BrainMappingUsingTopologyGraphsObtained
bySurfaceSegmentation
FabienVivodtzev,LarsLinsen,
BerndHamann,KennethI.Joy,BrunoA.Olshausen...................... 35
ComputingandDisplayingIntermolecularNegativeVolumeforDocking
ChangHaLee,AmitabhVarshney .................................... 49
OptimizedBoundingPolyhedra
forGPU-BasedDistanceTransform
RonaldPeikert,ChristianSigg ....................................... 65
Generating,Representing
andQueryingLevel-Of-DetailTetrahedralMeshes
LeilaDeFloriani,EmanueleDanovaro................................ 79
Split’NFit:AdaptiveFitting
ofScatteredPointCloudData
GregoryM.Nielson,HansHagen,KunLee,AdamHuang ................. 97
VIII Contents
PartII VolumeVisualizationandMedicalVisualization
RayCastingwithProgrammableGraphicsHardware
ManfredWeiler,MartinKraus,StefanGuthe,ThomasErtl,WolfgangStraßer..115
VolumeExplorationMadeEasyUsingFeatureMaps
KlausMueller,SarangLakare,ArieKaufman...........................131
FantasticVoyageoftheVirtualColon
ArieKaufman,SarangLakare .......................................149
VolumeDenoisingforVisualizingRefraction
DavidRodgman,MinChen .........................................163
EmphasizingIsosurfaceEmbeddings
inDirectVolumeRendering
ShigeoTakahashi,YurikoTakeshima,IsseiFujishiro,GregoryM.Nielson ....185
DiagnosticRelevantVisualization
ofVascularStructures
ArminKanitsar,DominikFleischmann,RainerWegenkittl,MeisterEduard
Gro¨ller .........................................................207
PartIII VectorFieldVisualization
CliffordConvolutionandPatternMatching
onIrregularGrids
JuliaEbling,GerikScheuermann.....................................231
FastandRobustExtraction
ofSeparationLineFeatures
XavierTricoche,ChristophGarth,GerikScheuermann ...................249
FastVortexAxisCalculationUsingVortexFeatures
andIdentificationAlgorithms
MarkusRu¨tten,Hans-GeorgPagendarm ...............................265
TopologicalFeaturesinVectorFields
ThomasWischgoll,JoergMeyer......................................287
PartIV VisualizationSystems
GeneralizingFocus+ContextVisualization
HelwigHauser ...................................................305
Contents IX
Rule-basedMorphingTechniques
forInteractiveClothingCatalogs
AchimEbert,IngoGinkel,HansHagen ................................329
APracticalSystemforConstrainedInteractiveWalkthroughs
ofArbitrarilyComplexScenes
LiningYang,RogerCrawfis .........................................345
ComponentBasedVisualisation
ofDIETApplications
RolfHendrikvanLengen,PaulMarrow,ThiesBa¨hr,HansHagen,Erwin
Bonsma,CefnHoile ...............................................367
FacilitatingtheVisualAnalysis
ofLarge-ScaleUnsteadyComputationalFluidDynamicsSimulations
KellyGaither,DavidS.Ebert........................................385
EvolvingDataflowVisualizationEnvironments
toGridComputing
KenBrodlie,SallyMason,MartinThompson,MarkWalkleyandJasonWood ..395
EarthquakeVisualizationUsingLarge-scaleGroundMotion
andStructuralResponseSimulations
JoergMeyer,ThomasWischgoll......................................409
AuthorIndex ....................................................433
Part I
Meshes for Visualization
Adaptive Contouring with Quadratic Tetrahedra
BenjaminF.Gregorski1,DavidF.Wiley1,HenryR.Childs2,BerndHamann1,and
KennethI.Joy1
1 InstituteForDataAnalysisandVisualization
UniversityofCalifornia,Davis
bfgregorski,dfwiley,bhamann,[email protected]
2 BDivisionLawrenceLivermoreNationalLaboratory
[email protected]
Summary. We present an algorithm for adaptively extracting and rendering isosurfaces
of scalar-valued volume datasets represented by quadratic tetrahedra. Hierarchical tetra-
hedral meshes created by longest-edge bisection are used to construct a multiresolution
C0-continuousrepresentationusingquadraticbasisfunctions.Anewalgorithmallowsusto
contourhigher-ordervolumeelementsefficiently.
1 Introduction
Isosurface extraction is a fundamental algorithm for visualizing volume datasets.
Most research concerning isosurface extraction has focused on improving the per-
formance and quality of the extracted isosurface. Hierarchical data structures, such
asthosepresentedin[2,10,22],canquicklydeterminewhichregionsofthedataset
containtheisosurface,minimizingthenumberofcellsexamined.Thesealgorithms
extract the isosurface from the highest resolution mesh. Adaptive refinement algo-
rithms[4,5,7]progressivelyextractisosurfacesfromlowerresolutionvolumes,and
controlthequalityoftheisosurfaceusinguserspecifiedparameters.
Anisosurfaceistypicallyrepresentedasapiecewiselinearsurface.Fordatasets
thatcontainsmooth,steepramps,alargenumberoflinearelementsisoftenneeded
to accurately reconstruct the dataset unless extra information is known about the
data. Recent research has addressed these problems with linear elements by using
higher-ordermethodsthatincorporateadditionalinformationintotheisosurfaceex-
tractionalgorithm.In[9],anextendedmarchingcubesalgorithm,basedongradient
information, is used to extract contours from distance volumes that contain sharp
features.Cellsthatcontainfeaturesarecontouredbyinsertingnewverticesthatmin-
imizeanerrorfunction.Higher-orderdistancefieldsarealsodescribedin[12].This
approachconstructsadistancefieldrepresentationwhereeachvoxelhasacomplete
descriptionofallsurfaceregionsthatcontributetothelocaldistancefield.Usingthis
representation,sharpfeaturesanddiscontinuitiesareaccuratelyrepresentedastheir
exact locations are recorded. Ju et al. [11] describe a dual contouring scheme for
4 B.F.Gregorskietal.
adaptivelyrefinedvolumesrepresentedwithHermitedatathatdoesnothavetotest
forsharpfeatures.Theiralgorithmusesanewrepresentationforquadricerrorfunc-
tions to quickly and accurately position vertices within cells according to gradient
information.Wileyetal.[19,20]usequadraticelementsforhierarchicalapproxima-
tionandvisualizationofimageandvolumedata.Theyshowthatquadraticelements,
insteadoflinearelements,canbeeffectivelyusedtoapproximatetwoandthreedi-
mensionalfunctions.
Higher-orderelements,suchasquadratictetrahedraandquadratichexahedra,are
used in finite element solutions to reduce the number of elements and improve the
qualityofnumerical solutions [18].Since few algorithmsdirectlyvisualize higher-
orderelements,theyareusuallytessellatedbyseverallinearelements.Conventional
visualization methods, such as contouring, ray casting, and slicing, are applied to
theselinearelements.Usinglinearelementsincreasesthenumberofprimitives,i.e.
trianglesorvoxels,thatneedtobeprocessed.Methodsforvisualizinghigher-order
elementsdirectlyaredesirable.
We use a tetrahedral mesh, constructed by longest-edge bisection as presented
in[5],tocreateamultiresolutiondatarepresentation.Thelineartetrahedralelements
usedinpreviousmethodsarereplacedwithquadratictetrahedra.Theresultingmesh
definesaC0-continuous,piecewisequadraticapproximationoftheoriginaldataset.
Thisquadraticrepresentationiscomputedinapreprocessingstepbyapproximating
thedatavaluesalongeachedgeofatetrahedronwithaquadraticfunctionthatinter-
polatestheendpointvalues.Aquadratictetrahedronisconstructedfromthecurves
alongitssixedges.Atruntime,thehierarchicalapproximationistraversedtoapprox-
imatetheoriginaldatasettowithinauserdefinederrortolerance.Theisosurfaceis
extracteddirectlyfromthequadratictetrahedra.
The remainder of our paper is structured as follows: Section 2 reviews related
work. Section 3describes what quadratic tetrahedra are, and Sect. 4describes how
theyareusedtobuildamultiresolutionrepresentationofavolumedataset.Sections5
describeshowaquadratictetiscontoured.OurresultsareshowninSect.6.
2 PreviousWork
Tetrahedral meshes constructed by longest-edge bisection have been used in many
visualization applications due to their simple, elegant, and crack-preventing adap-
tiverefinementproperties.In[5],fine-to-coarseandcoarse-to-finemeshrefinement
is used to adaptively extract isosurfaces from volume datasets. Gerstner and Pa-
jarola[7]presentanalgorithmforpreservingthetopologyofanextractedisosurface
usingacoarse-to-finerefinementschemeassuminglinearinterpolationwithinatetra-
hedron.Theiralgorithmcanbeusedtoextracttopology-preservingisosurfacesorto
perform controlled topology simplification. In [6], Gerstner shows how to render
multipletransparentisosurfacesusingthesetetrahedralmeshes,andin[8],Gerstner
andRumpfparallelizetheisosurfaceextractionbyassigningportionsofthebinary
tree created by the tetrahedral refinement to different processors. Roxborough and
Nielson [16] describe a method for adaptively modeling 3D ultrasound data. They