Tutorial TH2: Modeling and Rendering of Synthetic Plants
Oliver Deussen, Dresden University of Technology
Published by
The Eurographics Association
ISSN 1017-4565
23rd Annual Conference
EUROGRAPHICS 2002
Saarbrücken, Germany September 2–6, 2002
Organized by
EUROGRAPHICS T
HEE
UROPEANA
SSOCIATIONFOR
C
OMPUTERG
RAPHICS
INFORMATIK Max-Planck-Institut
für Informatik Saarbrücken, Germany
S A
RA V I E NSIS UNI VE R S I T
A S
Universität des Saarlandes Germany
International Programme Committee Chairs George Drettakis (France)
Hans-Peter Seidel (Germany)
Conference Co-Chairs Honorary Conference Co-Chairs Frits Post (The Netherlands) Jose Encarnação (Germany) Dietmar Saupe (Germany) Wolfgang Straßer (Germany)
Tutorial Chairs STAR Report Chairs
Sabine Coquillart (France) Dieter Fellner (Germany) Heinrich Müller (Germany) Roberto Scopignio (Italy)
Lab Presentation Chairs Industrial Seminar Chairs Günther Greiner (Germany) Thomas Ertl (Germany) Werner Purgathofer (Austria) Bernd Kehrer (Germany)
Günter Enderle Award Committee Chair Conference Game Chair François Sillion (France) Nigel W. John (UK)
John Lansdown Award Chair Conference Director
Huw Jones (UK) Christoph Storb (Germany)
Short/Poster Presentation Chairs Local Organization Isabel Navazo (Spain) Annette Scheel (Germany) Philipp Slusallek (Germany) Hartmut Schirmacher (Germany)
Mo delling and Rendering of Synthetic P lants Oliver Deussen F acult y of Computer Science Dresden Universit y o f T echnology , Germany [email protected] -dresden.de www.inf.tu-dresd en.de/cgm, www.greenw or ks.de
Intro duction Plant geometries:
→imp ortant fo r many indo or and outdo or scenes
→ha rd to generate and to manipulate
→tricky to convert
→to da y: seldomly u sed
→tomo rro w: standa rd of most animation systems
IntroductionContent
→Plant mo delling, a survey
→Mo delling o f ecosystems
→Rendering ecosystems efficiently
→Non-realistic rendering of plants
Introduction2What to lea rn?
→Mo delling metho dologies
→Geometry generation and manipulation
→Advanced rendering techniques
→Some pr actical asp ects
IntroductionMo delling o f P lants
Plantmodelling1Mo delling metho dologies Tw o d ifferent m o d elling metho dologies: 1.
Proceduralmethods(Algo rithms) intuitive, simple p ar ameters, sp ecial solutions fo r single p lants 2.
Rule-basedSystems(fo rmal gramma rs) lo cal rules, abstract pa rameterization, general app roach
Plantmodelling2The B eginning
→Ulam, 1966: cellula r automatons used fo r branching structures
→Cell states ar e changed due to given rules
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3 BranchingstructureproducedbyUlam Plantmodelling3
A C ontinous Mo del
→Cohen, 1967 (Biologist): creation o f a continous m o d el
→Mo dels are pr ogrammed by F ortran programs (one pr ogram fo r each structure)
BranchingstructuresproducedbyCohen Plantmodelling4Mo del of Cohen:
•gro wth tak es p lace only at the end of br anches
•strength and angle o f gro wth is determined by current d irection, a densit y field p lus its gradient sa w ell as resistence against changes of angle.
•br anching tendency is m o dulated by a probabilistic value, computed by distance to br anching p lace and lo cal densit y field.
Plantmodelling5Mo delling Branching Regulation
→Fisher and Honda (1979), tw o biologists, w ork ed on br anching e.g. ho w trees manage not tw o overlap their branches
→tw o -dimensinonal mo dels, tw o metho ds of br anching regulation: 1. br anching is p erfo rmed only at place whith a m inimum distance to all o ther br anching p laces.
VerzweigungsbildungnachHonda Plantmodelling2. during branching successo rs receive different gro wth rates in dep endence to the lo cal situation
BranchingstructuresbyFisherandHonda Plantmodelling7Mo delling Issues
→efficient mo delling o f branching structures
→generation o f a va riet y o f tree mo dels Mo del o f A ono and Kunii (1984): 1. bina ry br anching, generated trees have a monop o d ial or symp o dial shap 2. length and diameter of br anches ar e reduced by a constant facto br anching angles ar e constant fo r all br anching levels. 3. child br anches ar e d irected into plane d efined by father br anch and its maximal gradient. 4. simultanous br anching at all end p oints o f the branches
Plantmodelling(a) (b)
ParametersinAonoundKunii’smodel: a)Parameterisationofbranches;b)Divergenceangle Plantmodelling9→
later app roaches mostly use elements o f this branching mo del Dra w backs o f the implemented app roach:
→only very simple leaves
→tree sk eleton is only sk etched by lines of different width
BranchingstructuresbyAonoandKunii Plantmodelling10P a rticle Systems
→Reeves and Blau (1985) visual mo dels needed fo r a movie
→trees: simple recursive br anching mo del
→p o st pr o cessing: intro duction o f randomness
→leaves: small balls with colo r and or ientation
→imp ortant fo r rendering: go o d colo rs and shado w What to lea rn?
→simple m o dels can generate very n ice images
Plantmodelling11Results:
OrientedparticlesystemsbyReevesundBlau Plantmodelling12A F ractal T ree Mo del
→Opp enheimer (1986): inspired by Mandelb rot
→recursive pr o cedure creates self simila r trees
→Used pa rameters:
→Branching angle
→Ratio b et w een size of father and child br anches
→Amount of taping along stem and br anches
→Numb er of br anches p er segment
→Deviation angle
Plantmodelling13procedurefractaltree() begin drawcurrentbranchsegment if(smallenough) thendrwaleaf elsebegin transformforcurrentbranch fractaltree() repeat begin transformforbranching fractaltree() end endend Plantmodelling
Result:
FractaltreebyOppenheimer Plantmodelling15Mo delling the ba rk: ho rizontal sa w teeth function + Bro w nian noise ba rk
(x,y)=sa wteeth
(N∗(x+R∗noise
(x,y))) →noise
(x,y): p erio dical in
x−and
y−direction
PlantmodellingGeometric M o delling
→Blo omenthal (1985): tries to imp rove tree geometry Mo delling metho d:
→control p o ints by recursive algo rithm
→connection o f p oints by S pline interp olation (C2)
→generation o f surface by circula r shap es p erp endicula r to spline
→main problem: natural lo oking branches.
→solution: saddle surface at br anches
→ba rk: B ump-Mapping (source is real ba rk)
Plantmodelling17Result:
Treegeometries,createdbyBloomthal Plantmodelling18A B otanic A pp roach
→de Reffy e (1988): simulate b iological gro wth in 3-d
→discrete mo del (one step p er g ro wth p erio d )
→along a sp rout segments are placed
→each sp rout can rest or die A bud (top of a sp roud) has three probabilit y values: 1. pr obabilit y to d ie 2. pr obabilit y to rest 3. pr obabilit y to branch
Plantmodelling19GrowthsimulationbydeReffye
Used P arameter:
•tree age
•gro wth sp eed of br anches in different levels
•numb er o f buds in each level
•pr obabilities fo r dying, resting, and branching
Plantmodelling20procedurebudtree() begin foreachtimestepdobegin forjedelivingbugdobegin ifbuddoesnotdieanddoesnotrest thenbegin generatesegment generateapicalleaf end foreachbuddo ifbudbranches thengeneratebranch end end end Plantmodelling21
Results:
BranchingstructuresbydeReffye PlantmodellingA M o rphologic Idea
→Leona rdo da Vinci: tree sk eleton is a combination o f strands each strand connects a leaf to a small ro o t
→at br anching: strands ar e d ivided and generate children
→geometric result: section area of father equals sum o f section ar eas of children
→Holton (1994): m o d elling metho d b ased on strands
→Numb er of strands determines thickness and length o f branches, numb er o f leaves and branching angle
Plantmodelling23Strand mo del:
StrandmodelbyHolton PlantmodellingResults:
ExampletreesbyHolton Plantmodelling25Another p ro cedural Metho d
→W eb er and P enn (1995): tree generato r using 5 0 p ar ameters
→very nice trees, m o del very complicated
TreescreatedbyWeberandPenn Plantmodelling26V o xel Plants
→Greene (1989): interaction b et w een plants and environment
→in this case: plants that gro w o n w alls etc.
→fo r each vo xel element: determine ho w m uch sunlight is intro duced
→sta rting from a manually given seedp o int a probabilistic algo rithm lets the p lant gro w
→the algo rithm sea rches the n ext vo xel that recieves enough light and directs the plant into that d irection
Plantmodelling27Results:
ExampleplantsbyGreene Plantmodelling28Results (cont.):
ExampleplantbyGreene Plantmodelling29Rule-based Mo delling of Plants
→a fo rmal rule base transfo rms a g iven initial state into an final state
→extremely compact data description fo r complex objects (data amplification)
→data amplification is a time-consuming pr o cess
→creation is mostly based o n lo cal generation rules
PlantmodellingData Amplification
→replacement rules m o d ify lo cal p ortions of the data description Examples:
•GraphsEdges or no des ar e replaced by given sub graphs
•ArrayField values or combinations of values ar e replaced by other values
•StringsCha racters in a string ar e replaced by strings
•GeometricObjectsGeometric primitives are replaced by mo re complex ones
Plantmodelling31Geometric R eplacement The von Ko ch curve, a classical example:
→lines ar e replaced by a set of lines (generato r)
→initial state is a triangle (initiato r)
→successive replication o f lines generates a complex figure
PlantmodellingLindenma y er-Systemes
→a Lindenma ye r-System or L-System uses string replacement
→it is defined by a fo rmal gramma r
G=(V,ω,P):
V: alphab et
ω: axiom, a non-empt y string
P: set of pr o ductions
→p er time step all p o ssible replacements ar e p erfo rmed in pa rallel
→main d ifference to Chomsky gramma rs
Plantmodelling33An Example Alphab et:
V={f,F,+,−}Axiom:
w=F−−F−−FRule base:
P={F::=F+F−−F+F}Amplification (t w o replacements) :
F−−F−−F F+F−−F+F−−F+F−−F+F−−F+F−−F+F F+F−−F+F+F+F−−F+F−−F+F−−F+F+F+F−−F+F− −F+F−−F+F+F+F−−F+F−−F+F−−F+F+F+F−−F+ F−−F+F−−F+F+F+F−−F+F Plantmodelling34Graphical Interp retation
→string is converted into a graphical description
→T urtle-metapho r (Przemisla w Prusinkiewic z)
→T urtle is moved over a dra wing p lane or within the 3 -d space
→state o f turtle: vecto r
(x,y,α)p o sition p lus angle
→commands of the string (cha racters) change these lo cal pa rameters
Plantmodelling35Dra w ing c ommands in 2-d F move turtle in current d irection
αwith distance
dand dra w line state:
(x,y,α)→(x+dcosδ,y+dsinδ,α)f move turtle in current d irection
αwith distance
dwithout dra wing state:
(x,y,α)→(x+dcosδ,y+dsinδ,α)+ increase angle ab out
δ:
(x,y,α)→(x,y,α+δ)- decrease angle ab out
δ:
(x,y,α)→(x,y,α+δ) Plantmodelling36Branching Structures
→at a branch one n eeds to sto re state o f the turtle
→extension o f L -System d escription: a state machine Pushdo wn-Automaton:
→a stack data structure sto res the turtle states
→access to stack: push and p o p co rresp onding cha racters: [ sto re current state
(x,y,α)on stack ] load current state
(x,y,α)from stack
Plantmodelling37Examples (a) (b) (c) (d)
FigurenδwP (a)525,7◦F{F::=F[+F]F[-F]F} (b)422,5◦F{F::=FF-[-F+F+F]+[+F-F-F]} (c)725,7◦X{X::=F[+X][-X]FX,F::=FF} (d)522,5◦X{X::=F[[X]+X]+F[+FX]-X,F::=FF} PlantmodellingDra w ing c ommands in 3-d
→turtle: n ow p o sition in 3 -d plus three rotation angles (
α,β,γ)
→differential angle
δma y change each of the three angles
→turtle state:
(x,y,z,M)with
Mrotation matrix dra wing commands: F move turtle in current d irection
αwith distance
ddra w line, change state f move turtle in current d irection
αwith distance
dwithout dra wing, change state + increase current angle
γby
δ, change state - increase current angle
γby
δ, change state
Plantmodelling39& increase current angle
βby
δ, change state
∧decrease current angle
βby
δ, change state
\increase current angle
αby
δ, change state / decrease current angle
αby
δ, change state
|turn ar ound
→additional commands fo r colo rs, width of br anches
→filled geometry:
{and
}define sta rt and end of path that is triangulated later
PlantmodellingExample
A::=[&FL!A]/////[&FL!A]///////[&FL!A] F::=S/////F S::=FL L::=[∧∧{−f+f+f−|−f+f+f}] Bush,definedbyPrusinkiewiczandLindenmayer Plantmodelling41Sto chastic and P a rametric Systems Sto chastic L-Systems:
→intro duce randomness into p lant generation
→fo r a cha racter mo re than one replacement rule is defined
→fo r each rule an application probabilit y is d efined P arametric L -Systems:
→pa rameterization o f commands:
F(w): move into current direction ab out
w →allo ws to change values during expansion
Plantmodelling42Example Bina ry tree: let b e
n=10,
δ=85◦,
R=1.456, axiom
w=A(1)) only one rule:
A(s)::=F(s)[+A(s/R)][−A(s/R)] Plantmodelling43Mo re Examples
PlantsfromPrusinkiewicz,Lindenmayer:Thealgorithmicbeautyofplants Plantmodelling44Mo delling P hyllotaxis u sing L-Systems Pro duction:
A(n)::=+(137.5)[f(√ n)∼D]A(n+1) →D: sub-system to define a small circle
→complete head of a sun flo w er is defined by sub L -systems fo r seeds
S, and different kinds of blossoms
RR,M,N,O,P →conditional L-System
A(n)::=+(137.5)[f(√ n)C]A(n+1) C(n):n≤440::=∼S C(n):440<n≤565::=∼R C(n):565<n≤580::=∼M C(n):580<n≤595::=∼N C(n):595<n≤610::=∼O C(n):610<n::=∼P Plantmodelling45Results:
PlantsfromPrusinkiewicz,Lindenmayer:Thealgorithmicbeautyofplants PlantmodellingAnimation with L-Systems
→differential L -Systems
→continous gro wth is describ ed by d ifferential equations
Animationofplantgrowth,courtesyofP.Prusinkiewicz Plantmodelling47Plant Interaction with Environment
→environmental sensitive L-Systems
Twoplantinteractions,courtesyofP.Prusinkiewicz PlantmodellingCombined p ro cedural and rule-based mo delling
Combinedmodelling1Plant Mo delling so fa r:
→pr o cedural plant m o d elling metho ds
→algorithmsthatcanbeparameterised →moreorlessspecific →oftenintuitiveparameters(e.g.age,vigour) →example:AMAP/Bionatics →rule-based plant mo delling metho ds
→L-Systems,iteratedfunctionsystems(plantimages) →generalmodellingscheme →abstractformalrepresentationforaplant Combinedmodelling2xfrog - A New M o delling P a radigm
→collab oration with Bernd L intermann (ZKM K ar lsruhe)
→plant is rep resented by a structure g raph
Nodes:comp onents that encapsulate data and algo rithms Comp onent ty p es: 1. generation o f geometry 2. multiplicat ion o f geometry 3. global mo delling
Links:generation rules
Combinedmodelling3Generation of Geometry
→leaves: textured surfaces (triangles, quads or triangulated surfaces)
→br anches: generalized cylinders (inspired by T o dd and Latham):
T1T2 Combinedmodelling4
Multiplication of Geometry
→three ty p es o f m ultiplicat ion:
HydracomponentWreathcomponentPhiballcomponent →pr oblem: plant structure is rep resented by graph structure and by multiplicat ion comp onents
⇒geometry generation in tw o stages
Combinedmodelling5Multiplication of Geometry (cont.)
Aro ot comp onent
Bcomp onent that generates a cylinder
Cmultiplicat ion comp onent, numb er o f copies: 3
Dcomp onent that generates a cylinder, recursion depth: 3
CombinedmodellingPlant Example I
Combinedmodelling7Plant Example II
CombinedmodellingPlant Example II I
Combinedmodelling9F urther Examples I
Combinedmodelling10F urther Examples II
Combinedmodelling11F urther Examples II I
Combinedmodelling12Video
Combinedmodelling13Breaking o f regula rit y
→pr oblem: so fa r generated plants are to o regula r
→and m o d elling d ep ends on lo cal rules o nly Solutions:
→intro duction o f randomness
→mo delling o f exceptions
→global mo delling (FFDs)
CombinedmodellingF unctional mo delling
→in multiplicat ion comp onents, many pa rameters are sto red as ranges.
→fo r each multiplied comp onent, an indivdual value is determined by interp olation
→this mechanism can b e mo dified by applying functions
→pa rt of these functions can b e iteration numb ers (affects b ending, see right)
Combinedmodelling15Exceptions
→simila r mechanism to functional m o d elling
→each multiplied comp onent receives its ow n identificati o n numb er
→in each comp onent it can b e sto red if its geometry is generated acco rding to the identificati o n numb er
→application : dead br anches and twigs
CombinedmodellingF reefo rm-defo rmation
→a sp ecial comp onent allo ws to defo rm the gro wing space
→this can b e done fo r all generated vertices or only fo r the tree sk eleton
center:needlestoolarge,right:betterresult(onlyskeltondeformed) Combinedmodelling17T ropisms
→allo w to d irect gro wth into a d esired direction
→linea r, circula r or freely g iven d irections can b e used
Combinedmodelling18Pruning
→gro wth is restricted to a given volume
→can b e implemented lik e cutting the br anches or as a tendency to stop gro wth
Combinedmodelling19Animation
→the set of pa rameters describ es the m o del completely
→different sets of pa rameters can describ e a plant fo r d ifferent ages (k eyframes)
→interp olation o f the pa rameter values allo ws to animate plant gro wth
Combinedmodelling20Mo delling e cosystems
Ecosystems1General outline
→co op eration: Stanfo rd Universit y, Universit y o f Calga ry
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La yo ut of the project:
•pa rametrized p lant mo dels
•sp ecification o f terrain and ground pa ramters
•sp ecification o f p lant p o sit- ions and pa rameters
•reduction o f generated geo- metry
•efficient rendering
EcosystemsVideo
Ecosystems3Sp ecification b y Simulation
→random initial p ositions
→simulation o f self thinning Y o da et al.:
log(m)=−3 2log(d)+const m: d ry w eight of plants
d: p lant densit y
→p o pulation b elo w curve
⇒numb er increases
→gro wth (increase o f w eight)
⇒selection
EcosystemsVideo
Ecosystems5Simulating Different Plant Sp ecies Complex m o d el:
→self thinning, d omination, continous seeding
→environmental facto rs (w ater, soil) Ground W ater Plant Distribution
Ecosystems6Resulting image (Premysla w Prusinkiewic z):
Ecosystems7Graphical Sp ecification
•user dra w s p opulations (densit y and other pa rameters)
•plant p ositions computed by va riant o f h alftoning
•each plants recieves individual p arameters
Ecosystems8Example
→gra y-scale images dra w n by the user Densit y P rosp erit y P ositions
Ecosystems9EcosystemsReduction of Geometry
→va riant o f traditional instancing (replace rep eated objects by instances of one rep resentative)
→here: replace simila r objects by a set o f rep resentatives
⇒ApproximateInstancing →10-15 rep resentatives p er sp ecies ar e enough fo r cheating a la rge visual complexit y.
Ecosystems11Video
EcosystemsExamples I
Ecosystems13Examples II
Ecosystems14Examples II I
Image:BerndLintermann Ecosystems15Examples IV
Ecosystems16Examples V
Image:BerndLintermann Ecosystems17Real-time rendering of virtual landscap e s
Level-of-detaijoint w or k with: Ca rsten Coldiz, Dresden T echnical Universit y Ma rc Stamminger, Universit y o f W eima r (Germany) Geo rge Drettakis, Reves/INRIA, Sophia A ntip olis (F rance) www-sop.inria.fr/rev es/index.gb.html
→will b e published in IEEE Visualization 2002 (Boston)
Level-of-detail2Problems W ith C omplex Plant Scenes
•unnecessa ry amount of va riet y
→approximateinstancing →reducestheamountofdifferentmodelsneededinascene •over-sp ecification
→simpleprimitiveslikepointsandlines →reducestheamountofverticestobeprocessed →visibilityculling/occlusionculling →unseenmodelsarenottransferredtothegraphicsprocessor •to o complex p lant mo dels
→level-of-detailalgorithms →eachplantisshownwithacomplexitythatissufficientforitsactualsize Level-of-detaiAn Example
→16,7 million p olygons
→ab out o ne million p ixels
⇒bunches o f p olygons p er pixel
Level-of-detail4LOD-Rep resentation fo r Plants
→static level-of-detai l:
→modelisrepresentedbyseveraldifferentrepresentationthatareblended →visiblepoppingartefactscanoccur →allrepresentationsmustbestored →dynamic level-of-detai l:
→modelrepresentationiscomputedindividuallyforeachsizeonthescreen →poppingartefactsarereduced →memoryefficient →representationmustbecomputedforeachimage Level-of-detail5Rep resentation b y P o ints and Lines
→compact objects (leaves, p etals)
⇒p o int rep resentation
→thin objects (b ranches, sp ecial leaves)
⇒line rep resentation
→p o int and line rep resentations ar e shuffled
→... and sto red in vertex arra ys
⇒va riing amount of data can b e used to rep resent a p lant
⇒blending b et w een p o lygonal mo del and app ro ximation is d one successively on the b asis of individual leaves
Level-of-detail6A P oint Rep resentation
Polygones13,000Points6,5003,2501,625 Level-of-detail7Single P lant Example
→small triangles: vertex rate counts
→standa rd w ay o f app ro ximation: replace p o lygons by p oints if pro- jected ar ea can b e displa ye d m or e efficiently by p o int app ro ximation
→displa y can b e tuned by p o int splat size and replacement threshold
standarddisplay Level-of-detail8Single P lant Example (cont.)
thresholdreduced:pointsvisible thresholdreduced,splatsizeincreased Level-of-detaiReduction p rinciple
Level-of-detail10Line R ep resentation
1,500plants,originalmodelsize12milliontriangles,nowdisplayedwith10-15Hz Level-of-detaiVideo
Level-of-detail12Imp o rtance R eduction
→imp ortant pa rts o f p lants (blossoms, p etals) ar e not so much reduced
⇒qualit y can b e increased without m uch m or e d ata
Level-of-detail13Complex S cene Examples
100millionpolygones/8Hz70millionpolygones/4Hz →here: scene hiera rchy used to enhance LOD rep resentation
Level-of-detail14Video
Level-of-detail15Nonrealistic R endering of Plants
Nonrealisticrendering1Images of T rees
Drawings:LarryEvans NonrealisticrenderingExample I A g ar den, dra w n at b eginning of 20th centruy:
Drawing:LarryEvans Nonrealisticrendering3Example II
Drawing:LarryEvans NonrealisticrenderingMo dern illustrations:
Drawing:LarryEvans Nonrealisticrendering5Drawing:LarryEvans Nonrealisticrendering6
Synthetic Plant Sk etches
→Ho w d o artists w ork?
→first observation: abstraction o f shap e
Drawing:LarryEvans Nonrealisticrendering7→
shap e by agglomeration o f small entities
Drawing:LarryEvans Nonrealisticrendering8→
light and shado w by adding detail
Drawing:LarryEvans Nonrealisticrendering9→
light and shado w by h atching
Drawing:LarryEvans NonrealisticrenderingSynthetic Illustrations
→sta rting from realistic plant m o d els (xfrog)
→tree sk eleton and leaves must b e handled differently Data rep resentation:
•tree sk eleton: thinned geometry
•leaves: oriented p oint cloud
→this enables flexible, fast and coherent illustration
Nonrealisticrendering11Algo rithm T ree sk eleton: conventional algo rithms of NPR (silhouettes + hatching) Leaves:
→abstract dra wing primitives rep resent foliage
→depth d ifferences control detail of dra wing
→dra wing st yles by va riaton o f abstract dra wing primitives
NonrealisticrenderingExample
→a la rge tree
Nonrealisticrendering13Example (cont.)
→rep resentation o f tree sk eleton
Nonrealisticrendering14Example (cont.)
→rep resentation o f foliage (p oint cloud)
Nonrealisticrendering15Example (cont.)
→hatching of stem
Nonrealisticrendering16Example (cont.)
→tw o rep resentations of the same tree (size=0.15, DDT=1000) (size=0.7, DDT=2000)
Nonrealisticrendering17Video
NonrealisticrenderingExample (cont.)
→non-linea rit y of depth buffer
⇒LOD can b e achieved automaticall y
→in the b ackground differences ar e relatively la rger
Nonrealisticrendering19Example (cont.)
→additionall y: enla rge primitives in the background
NonrealisticrenderingVideo
Nonrealisticrendering21Example (cont.)
→va riation o f primitive shap e
→p erfo rmed by interp olation due to no rmal vecto r
Nonrealisticrendering22Results
Nonrealisticrendering23A la rger example
Nonrealisticrendering24Video
Nonrealisticrendering25NonrealisticrenderingF u rther Readings
→B. Lintermann, O. Deussen:
InteractiveModellingofPlantsIEEE Computer Graphics and A pplication s, 19(1), pp. 56–65, 1999
→O. Deussen, P . Hanrahan, M. Pha rr, B. Lintermann, R. M ˇech, P . Prusinkiewic z:
RealisticModellingandRenderingofPlantEcosystemsSIGGRAPH 9 8 Conference Pro ceedings, pp. 275–286
→O. Deussen, C. Colditz, M . S tamminger, G . D rettakis:
InteractiveVisualizationofComplexPlantEcosystemsIEEE Visualization 2002, Boston, in print, see: www.inf.tu-dresd en.de/cgm
Nonrealisticrendering27→
O. Deussen:
ComputergeneriertePflanzenSp ringer-V erlag 2002 (in print)
NonrealisticrenderingReferences
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