Post on 26-Mar-2015
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Preprocessing ofLarge databasesV for
Interactive visualisation
Preprocessing ofLarge databasesV for
Interactive visualisation
Xavier Décoret
iMAGIS-GRAVIR / IMAG
iMAGIS est un projet commun CNRS - INPG - INRIA - UJF
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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ContextContext
• Virtual environments– Video game, virual tourism, simulations
• User walk freely through the modl
• The computer is in charge of generating images of what user « sees »
Frequent refresh (25 / sec)
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Feeling of immersionFeeling of immersion
• Complex environments– Large spatial extent– Highly detailed
• Realistic effects– Shadows– Ligthing effets (reflection)– Appearance
High computation time
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Useractions
Useractions
ContextContext
RenderingSystem
RenderingSystemDatabaseDatabase imagesimages
Model complexity Bounded computation time
Preprocess to speed-up•Reusing results
•Optimizing representations
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Hidden Faces RemovalHidden Faces Removal
• Vertex projections• Face rasterisation
View frustum
8Image
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
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Pixel
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
10Image
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
11Image
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
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• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
13Image
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
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Pixel =ColorDepth
depth
• Vertex projections• Face rasterisation
Hidden Faces RemovalHidden Faces Removal
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depth > depth
• Vertex projections• Face rasterisation• Z-buffer [Cat74]
Hidden Faces RemovalHidden Faces Removal
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ConsequencesConsequences
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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ConsequencesConsequences
Image
• Complex 3D model ) lot of calculations
• Redundancy in computations
• Unadapted computations
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Possible solutionsPossible solutions
• Visibility computations– Finding what is hidden– Prevent unecessary rasterization
• Level of Details– Several level of modelisation– Using the level fitted to object’s distance
• Alternative rendering
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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Visibility computationVisibility computation
• Reject as soon as possible what will not
contribute to an image
• Two approaches– Online ) for current view point
– Offline ) for a region of space
• Difficulty: umbrae and penumbrae fusion
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Umbrae fusionUmbrae fusion
Viewpoint
Shadow volume
Buildings(top view)
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Shadow volume
Viewpoint
Buildings(top view)
Umbrae fusionUmbrae fusion
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Shadow volume
Viewpoint
Buildings(top view)
Umbrae fusionUmbrae fusion
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Viewpoint
Buildings(top view)
Umbrae fusionUmbrae fusion
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Penumbrae fusionPenumbrae fusion
Viewcell
Buildings(topview)
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Penumbrae fusionPenumbrae fusion
Viewcell
Buildings(topview)
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VisibilityVisibility• Lot of previous work [Dur99]• Classification [SPS74]
Image SpaceObject Space
•Hierarchical Frustum Culling [GBW90]
•Shaft culling [HW91]
•Shadow volumes [CT97]
•Bloqueurs convexes [CZ98]
•Convex Vertical Prisms [DM01]
•Volumetric visibility [SDSD00]
•Portals [ST91]
•Hierarchical Z-buffer [GKM93]
•Hierarchical Occlusion Map [ZMH97]
•2D1/2 Occlusion maps [WS99]
•Extended projections [DDTP00]
•Line Space subdivision [BWW01]
•Portals [LG95]
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Complexe problemComplexe problem
• No exact solution ) being conservative
• Umbrae fusion more or less done
• Object space ) extended visibility
• Image space ) fusion (implicit)
Combining approaches
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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DifficultyDifficulty
• Visibility from-point easy– Z-buffer
• Visibility from region difficult
Reducing to a from-point problem
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Blocker shrinkingBlocker shrinking
• Proposed by [WWS00]
Viewcell
Object
Blockers
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Object
Shrunk blockers
Center of viewcell
• Proposed by [WWS00]
Blocker shrinkingBlocker shrinking
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O
• Proposed by [WWS00]
Blocker shrinkingBlocker shrinking
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O { P such as Br(P) O }
r-shrinking
• Proposed by [WWS00]
Blocker shrinkingBlocker shrinking
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O
V
M
• Generalisation to convex viewcells
• Shrinking of occludees
V’
• Proposed by [WWS00]
Blocker shrinkingBlocker shrinking
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Occluder/occludees shrinkingOccluder/occludees shrinking
Viwcell
Object
Blockers
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Shrunk blockers
Center ofviewcell
Shrunk object
Image taken fom viewcell’s center
with shrunk objects
•Same treatment to occluders/occludees•One pass algorithm
Occluder/occludees shrinkingOccluder/occludees shrinking
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Formalisation (1)Formalisation (1)
• Dilatation (Minkowski sum)
Set of points
O
Set of vectors
XO © X
{P+x, P2 O and x 2 X}
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Formalisation (2)Formalisation (2)
• Erosion
Set of points
O
Set of vectors
X
O ª X
{P such as 8 x 2 X, P+x 2 O }
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TheoremTheorem
If a ray (VM) is blocked by
O ª X with X convex, then:
Any ray (V’M’) is blocked
by O with:
V’ 2 {V} © X and
M’2 {M}© X
V
MV’
M’
O ª X
O
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Approximative erosionApproximative erosion
• Exact erosion is hard to compute
• We can have approximations
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DifficultyDifficulty
O ª X
Erosion by X
O ª X
Internal erosion
½ O ª X
External erosion
½
• Exact erosion is hard to compute
• We can have approximations
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Mise en oeuvreMise en oeuvre
• Building an occlusion map with internal erosions
• Testing external erosions against the map
Objects+erosions
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Mise en oeuvreMise en oeuvre
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
Visibles
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
Visibles
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
Visibles
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
Visibles
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Modification de l’algorithmeModification de l’algorithme
Carte d’occlusion
Visibles
Hidden
• Building an occlusion map with internal erosions
• Testing external erosions against the map
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Pros & consPros & cons
Two pass of rendernig (map + test)
Tests can be done par graphic card
Linear complexity
Linear memory cost
ObjectsObjects
2 pass2 passApproximative erosion
Approximative erosion
Exact erosionExact erosion 1 pass1 passVisibility
pre-computation
Visibility pre-computation
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Approximative erosionApproximative erosion
• Voxelisation of object– Volumetric information [SDDS00]– Suitable representation [DM01]
• Erosion on voxels– Simple– Robust and fast
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VoxelisationVoxelisation
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VoxelisationVoxelisation
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VoxelisationVoxelisation
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Erosion of voxels by a cubeErosion of voxels by a cube
= ©
= ©©
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O ª (X ©Y) = (O ª X) ª Y
=ª
ª ª ª
Erosion of voxels by a cubeErosion of voxels by a cube
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Erosion 1DErosion 1D
• Of half a voxel
Direction of erosionTopological
change
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Erosion 1DErosion 1D
• Of half a voxel
Direction of erosion
• Of less than a half
Topological change
Topology preserved
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=ª
ª ª ª
Aligned axis
Erosion of voxels by a cubeErosion of voxels by a cube
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Erosion of voxels by X convexErosion of voxels by X convex
Cellule X
voxels
If X ½ Y then O ª Y ½ O ª X
ªInternal erosion
)
ªExternal erosion
)
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DemoDemo
• Erosion of voxels
• Visibility pre-computation
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ConclusionConclusion• Formalism and new theorem
– Érosion of occluders and occludees
• Per object voxelisation– Optimized orientation– Do no discretize empty spaces
• Working in image space– Implicit fusion of umbrae– Acceleration
• Hardware : graphic cards• Software : combining with other visibility algorithm
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Ext step…Ext step…
We know what is visible
How to display it?
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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Level of detailsLevel of details
• Mesh simplification
•Clusterisation [RB93,LT97]
•Hierarchical Dynamic Simplification [LE97]
•Decimation of Triangle Meshes [SZL92]
•Re-tiling [Tur92]
•Progressive Meshes [Hop96,PH97]
•Quadric Error Metrics [GH97]
•Out of Core Simplification [Lin00]
•Re-tiling [Tur92]
•Voxel based reconstruction [HHK+95]
•Multiresolution analysis [EDD+95]
•Superfaces [KT96], face cluster [WGH00]
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LimitationsLimitations
• Constraints on models• Erreur contrôle
– Simplification enveloppes [CVM96]– Permission Grids [ZG02]– Image driven [LT00]
• Handling of attributes (textures and colors)– Integration to the metric[GH98][Hop99]– Re-generation [CMRS98,COM98]
• Extreme Simplification– Sillouhette Clipping [SGG+00]
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Alternative renderingAlternative rendering
• Image based rendering– Lightfield,Lumigraph [LH96,GGRC96]
– Imposteurs [DSSD99]
– Relief Textures [OB00]
• Point based rendering– Surfels [PZBG00]
– Pointshop 3D [ZPKG02]
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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Billboards cloudBillboards cloud
• New representation
• Used for extreme simplification
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BillboardBillboard
• Classical solution [RH94]
• Generalising to many planes• Automating synthesis
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OverviewOverview
• Approaching shape by a set of plane
• Projecting model on those planes) textures
• Textures interleaving replace the object
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PrinciplePrinciplepolygonal 3D model
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PrinciplePrinciple
Simplification by planes
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PrinciplePrinciple• Moving vertices
Maximum allowed displacement for P
P
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PrinciplePrinciple• Projecting polygons on planes
Polygon
Valide plane
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PrinciplePrinciple• How many planes? Which planes?
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OverviewOverview• It is an optimisation problem
• Measuring plane interest
• Traversing the space of planes
• Finding a set of planes
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OverviewOverview• It is an optimisation problem
– Greedy algorithm
• Measuring plane interest
• Traversing the space of planes
• Finding a set of planes
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OptimisationOptimisation•We define over the set of Billboards clouds:
– An error function– A cost function
•Two goals– Budget-based
cost fixed minimising error
– Error-based max error fixed minimising cost
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OptimisationOptimisation•We define over the set of Billboards clouds:
– An error function– A cost function
•Two goals– Budget-based
cost fixed minimising error
– Error-based max error fixed minimising cost
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OptimisationOptimisation
• Cost function– Number of planes
• Error function– Vertex displacement
• In object space
• In image space
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OverviewOverview• It is an optimisation problem
– Greedy algorithm
• Measuring plane interest– Defining a density function
• Traversing the space of planes
• Finding a set of planes
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Replaces a lot of faces
Fonction de densitéFonction de densité• Important plane = low cost
Density function overThe space of planes
• density = measure of the amount of facesthat a plane can replace
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ValiditéValidité• Faces for which a plane is valid
– Enforces the error bound
• Density = number of valid faces
Allowed displacement
Density de 3
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ValiditéValidité
Allowed displacement
Density of 3
• Faces for which a plane is valid– Enforces the error bound
• Density = number of valid faces
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ContributionContribution• Ponderation by projected area
– Favor large faces– Favor planes parallel to faces
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OverviewOverview• It is an optimisation problem
– Greedy algorithm
• Measuring plane interest– Defining a density function
• Traversing the space of planes– discretisation
• Finding a set of planes
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DiscretisationDiscretisation• Discretisation of plane space
• Hough transform
ρ
φ
θ(θ,φ)
O
ρ
primal dual
H
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Dual spaceDual space• planes through a point ) a sheet
φθ
ρ
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• Plans through a sphere ) a slice
φθ
ρ
Dual spaceDual space
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• Plans through a sphere ) a slice
• Planes through 3 spheres ) intersection of 3 slices
φθ
ρ• Uniform discretisation
Dual spaceDual space
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Cumulated densityCumulated density
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OverviewOverview• It is an optimisation problem
– Greedy algorithm
• Measuring plane interest– Defining a density function
• Traversing the space of planes– discretisation
• Finding a set of planes– Refinement
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Greedy iterationGreedy iteration
Faces
Plane space
Planes validPlanes validfor the facefor the face
DiscretisationDiscretisation
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Faces
Plane space
Planes validPlanes validfor the facefor the face
DiscretisationDiscretisation
DensityDensity
+
-
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Faces
Planes validPlanes validfor the facefor the face
DensityDensity
+
-
Plane space
DiscretisationDiscretisation
Greedy iterationGreedy iteration
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Cell of highestdensity
Faces for which cell is valid
Greedy iterationGreedy iteration
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High density
There is probably a plan valid for all the faces
How to find such a plane?
Greedy iterationGreedy iteration
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We test centralplane
We subdivide
Local densityrecomputation
Greedy iterationGreedy iteration
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Texture synthesisTexture synthesis
• To each plane is associated a set of faces
• Orthogonal projection on plane
• Minimal bounding rectangle (CGAL)
• Orthogonal rendering ) texture
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ResultsResults
• Movies
Examples Shadows
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View-dependent extensionView-dependent extension
• Changing the error function– Reprojection error
P-
M P+
viewcell
V
T
θ
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View-dependent extension View-dependent extension
• Textures rendered from viewcell’s center
• Automatic selection of resolution
• Saving the projection matrix
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ResultsResults
Close
zoom
View from the cell
Billboards cloud polygonal model
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MiddleRange
ResultsResults
zoom
View from the cell
Billboards cloud polygonal model
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Far
ResultsResults
zoom
View from the cell
Billboards cloud polygonal model
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ConclusionConclusion
• New representation
• Automatic construction
• Arbitrary models
• Simple error criteria / no parameter
• Extreme simplification
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ExtensionsExtensions
• Optimising texture usage– Integration to the cost function– Texture compression
• Re-lighting– Normal maps– Pixel shading
• Transition• Moving objects
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SummarySummary
• Context• Visibility computation
– Previous work– Contributions
• Level of details– Previous Work– Billboard clouds
• Conclusion
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ConclusionConclusion
• New tools for the studied proble,• Visibility computation
– Theoretical results
– Practical algorithm easy to implement
• Level of details– New representation / Algorithm for construction
– Extreme simplification / handling of attributes
• Integration
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QuestionsQuestions