1 preprocessing of large databasesv for interactive visualisation xavier décoret imagis-gravir /...

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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




• 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



9Image

Pixel



10Image



11Image



12Image



13Image



14Image

Pixel =ColorDepth

depth



15Image

depth > depth

• Vertex projections• Face rasterisation• Z-buffer [Cat74]


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ConsequencesConsequences

• Complex 3D model ) lot of calculations

• Redundancy in computations

• Unadapted computations

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Image




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Image




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Image




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Image




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Image




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Image




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Image




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Image




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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




• 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)


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Shadow volume

Viewpoint

Buildings(top view)


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Viewpoint

Buildings(top view)


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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




• 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



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O



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O { P such as Br(P) O }

r-shrinking



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O

V

M

• Generalisation to convex viewcells

• Shrinking of occludees

V’



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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



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Modification de l’algorithmeModification de l’algorithme

Carte d’occlusion



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Carte d’occlusion



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Carte d’occlusion



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Carte d’occlusion



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Carte d’occlusion

Visibles



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Carte d’occlusion

Visibles



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Carte d’occlusion

Visibles



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Carte d’occlusion

Visibles



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Carte d’occlusion

Visibles

Hidden



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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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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


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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


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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




• 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




• 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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– Greedy algorithm

• Measuring plane interest



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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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• Measuring plane interest– Defining a density function



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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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• Traversing the space of planes– discretisation


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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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• 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



DensityDensity

+

-


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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Faces


DensityDensity

+

-

Plane space



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Cell of highestdensity

Faces for which cell is valid


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High density

There is probably a plan valid for all the faces

How to find such a plane?


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We test centralplane

We subdivide

Local densityrecomputation


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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


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Far

ResultsResults

zoom

View from the cell


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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




• 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

1 preprocessing of large databasesv for interactive visualisation xavier décoret imagis-gravir /...

Documents

problem slide

sec slide

penumbrae fusion slide

view umbrae fusion slide

inria ujf slide

consequences image complex

consequences complex

difficulty visibility