image-based rendering to accelerate interactive walkthroughs
DESCRIPTION
Lucent Technologies. Bell Labs Innovations. Image-Based Rendering to Accelerate Interactive Walkthroughs. Daniel G. Aliaga Visual Communications Research Lucent Technologies Bell Labs . 3D Models. 2.0M tris. 1.7M tris. 1.0M tris. 0.9M tris. Why Use Images?. - PowerPoint PPT PresentationTRANSCRIPT
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Image-Based Rendering Image-Based Rendering to Accelerate to Accelerate Interactive Interactive
WalkthroughsWalkthroughs
Lucent TechnologiesBell Labs Innovations
Daniel G. AliagaDaniel G. Aliaga
Visual Communications Visual Communications ResearchResearch
Lucent Technologies Bell LabsLucent Technologies Bell Labs
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3D Models3D Models
1.7Mtris
0.9Mtris
2.0Mtris
1.0Mtris
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Why Use Images?Why Use Images?
• Independent of scene complexityIndependent of scene complexity
640x480 pixels640x480 pixels 640x480 pixels640x480 pixels
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Flight SimulatorsFlight Simulators
• Mid-1980’sMid-1980’s– E&S CT-6 one of first to use real-time E&S CT-6 one of first to use real-time
photo texturesphoto textures
• Hand-selected objects:Hand-selected objects:– Terrain, trees, airplanes, buildings, Terrain, trees, airplanes, buildings,
etc.etc.
• 30-60 Hz30-60 Hz– High visual fidelityHigh visual fidelity
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OverviewOverview
• Replacing Geometry with ImagesReplacing Geometry with Images• Displaying ImagesDisplaying Images
– Texture-mapping and error metricsTexture-mapping and error metrics– Geometry and image warping Geometry and image warping – Meshes, Lightfield/LumigraphMeshes, Lightfield/Lumigraph
• Image PlacementImage Placement– Automatically Bounding Model ComplexityAutomatically Bounding Model Complexity– Cells and PortalsCells and Portals
• ConclusionsConclusions
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Replacing Geometry with Replacing Geometry with ImagesImages
• AlgorithmAlgorithm– Select subset of modelSelect subset of model– Create image of the subsetCreate image of the subset– Cull subset and replace with imageCull subset and replace with image
• Why?Why?– Image displayed in (approx.) constant Image displayed in (approx.) constant
timetime– Image reused for several framesImage reused for several frames
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Simple ExampleSimple Example
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Simple ExampleSimple Example
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Simple ExampleSimple Example
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OverviewOverview
• Replacing Geometry with ImagesReplacing Geometry with Images• Displaying ImagesDisplaying Images
– Texture-mapping and error metricsTexture-mapping and error metrics– Geometry and image warping Geometry and image warping – Meshes, Lightfield/LumigraphMeshes, Lightfield/Lumigraph
• Image PlacementImage Placement– Automatically Bounding Model ComplexityAutomatically Bounding Model Complexity– Cells and PortalsCells and Portals
• ConclusionsConclusions
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Geometric Geometric DiscontinuityDiscontinuity
• If we move from the center-of-projection, If we move from the center-of-projection, discontinuities appear at the borderdiscontinuities appear at the border
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Temporal DiscontinuityTemporal Discontinuity
• While moving, if we switch between While moving, if we switch between geometry and image, a sudden geometry and image, a sudden poppop occurs occurs
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ApproachesApproaches
• Geometric and Temporal Geometric and Temporal ContinuityContinuity– Error metricsError metrics– Geometry warpingGeometry warping– Image warpingImage warping– Lightfield/LumigraphLightfield/Lumigraph
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Error MetricsError Metrics
• Use an Use an error metricerror metric to control to control amount of discontinuityamount of discontinuity
c1
c2
[Maciel95][Shade96][Schaufler96]
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Error MetricError Metric
• Relies on “angular-deviation” Relies on “angular-deviation” measuring the visual quality of measuring the visual quality of using the (same) imageusing the (same) image
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Video Segment IVideo Segment I
• Pre-rendered ImpostorsPre-rendered Impostors– Maciel95Maciel95
• Dynamic Image-CachingDynamic Image-Caching– Shade96, Schaufler96Shade96, Schaufler96
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Geometry WarpingGeometry Warping
[Aliaga96]
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
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Geometry WarpingGeometry Warping
Surrounding geometry warpedSurrounding geometry warped(incorrect perspective)(incorrect perspective)
Correct perspectiveCorrect perspective
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Geometry WarpingGeometry Warping
• Surrounding geometry warped to Surrounding geometry warped to match imagematch image
Center-of-projection
Viewpoint
SurroundingGeometry
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Video Segment IIVideo Segment II
• Geometry WarpingGeometry Warping– Aliaga96Aliaga96
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Image WarpingImage Warping
• Change the image itselfChange the image itself– Re-project the image to the current Re-project the image to the current
viewpointviewpoint• [Chen93][McMillan95][Max95][Shade98][Chen93][McMillan95][Max95][Shade98]
– Display image as a (simplified, Display image as a (simplified, textured) meshtextured) mesh• [Darsa97][Sillion97][Darsa97][Sillion97]
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Image WarpingImage Warping
• A raster scan of each sheet produces a A raster scan of each sheet produces a back-to-front ordering of warped pixelsback-to-front ordering of warped pixels
eye
Reference COP
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Image WarpingImage Warping
• McMillan and Bishop’s Warping McMillan and Bishop’s Warping EquationEquation
~Texture mappingMove pixels based on distance to eye
xx22 = = (x(x11) P) P22-1-1
(c(c11 - c - c22) + P) + P22-1-1
PP1 1 xx11
x1c1
c2x2
d
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Example...Example...
• Image outlined in yellowImage outlined in yellow• Viewed from image’s center-of-projectionViewed from image’s center-of-projection
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3D Image Warp3D Image Warp
• Single sample per pixelSingle sample per pixel
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Layered Depth Image Layered Depth Image WarpWarp
• Multiple samples per pixel Multiple samples per pixel – Previous occlusions are filled-inPrevious occlusions are filled-in
[Popescu98]
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MeshesMeshes
• (Simplified) Textured Depth Mesh(Simplified) Textured Depth Mesh– Per-pixel depth creates mesh that Per-pixel depth creates mesh that
approximates 3D parallax effectsapproximates 3D parallax effects– Image is texture-mapped onto meshImage is texture-mapped onto mesh
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Video Segment IIIVideo Segment III
• 3D Image Warping3D Image Warping– McMillan95McMillan95
• Textured MeshesTextured Meshes– Darsa97, Sillion97Darsa97, Sillion97
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Lightfield/LumigraphLightfield/Lumigraph
• Flow of light at all positions and Flow of light at all positions and directionsdirections– [Levoy96][Gortler96][Levoy96][Gortler96]
• Large number of images are used as Large number of images are used as 2D slices of a 4D light function2D slices of a 4D light function
(u,v)
(s,t)
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Video Segment IVVideo Segment IV
• Light fieldLight field– Levoy96Levoy96
• LumigraphLumigraph– Gortler96Gortler96
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OverviewOverview
• Replacing Geometry with ImagesReplacing Geometry with Images• Displaying ImagesDisplaying Images
– Texture-mapping and error metricsTexture-mapping and error metrics– Geometry and image warping Geometry and image warping – Meshes, Lightfield/LumigraphMeshes, Lightfield/Lumigraph
• Image PlacementImage Placement– Automatically Bounding Model ComplexityAutomatically Bounding Model Complexity– Cells and PortalsCells and Portals
• ConclusionsConclusions
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Automatic Image-Automatic Image-PlacementPlacement
• As a preprocessAs a preprocess– Select geometry to replace with an Select geometry to replace with an
image in order to limit the number of image in order to limit the number of primitives to render for any frameprimitives to render for any frame
• At run timeAt run time– Display selected geometry as a Display selected geometry as a
(depth) image(depth) image– Render remaining geometry normallyRender remaining geometry normally
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Automatic Image-Automatic Image-PlacementPlacement
PreprocessPreprocess
Run timeRun time
ModelAutomatic
Image-Placement
Create Images
Cull GeometryReplaced with
Images
RenderGeometry and
ImagesDisplayModel
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Example RenderingExample Rendering
Final Scene
Geometry
Image
+ =
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Viewpoint
- Overview- Image Placement- Displaying Images- ConclusionsKey ObservationKey Observation
• Example modelExample model• Too much Too much
geometry in geometry in view frustumview frustum
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Viewpoint
Key ObservationKey Observation
• Geometry is Geometry is replaced by replaced by image to limit image to limit the number of the number of primitives to primitives to renderrender
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Eye
Grid viewpoint
Key ObservationKey Observation
• Less geometry Less geometry is in the view is in the view frustum from frustum from the eye than the eye than the one from the one from the grid the grid viewpoint viewpoint
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Recursive Subdivision Recursive Subdivision AlgorithmAlgorithm
2k 2k+1
2k+22k+1
Even to odd
Odd to even
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Example GridExample Grid
3D grid of 1557 viewpoints3D grid of 1557 viewpoints
-1
-0.50
0.51
-1
-0.5
0
0.5
1-1
-0.5
0
0.5
1
Wireframe renderingWireframe rendering
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Sample PathSample Path
0
100000
200000
300000
400000
500000
600000
700000
0 50 100 150 200 250 300Frame
View Frustum CullingImages Present
Pol
ygon
s
Power Plant, 2M tris, SGI Onyx2 w/ IR
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Images Per TriangleImages Per Triangle
0
0.001
0.002
0.003
0.004
0 10 20 30 40 50
Maximum Rendered Triangles (% of Model)
Imag
es P
er M
odel
Tri
angl
e
Power PlantTorpedo RoomBrooks HousePipesBest CaseWorst Case
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Preprocessing Preprocessing SummarySummary
Model No. of Images Prep. Time(hours)
EstimatedSpace (MB)
Power Plant(2M)
239-5815 1.2-21.7 156-3802
Torpedo Room(850k)
181-2333 1.1-11.8 72- 933
Brooks House(1.7M)
561-2492 11.4-28.4 388-1725
Pipes(1M)
282- 893 2.4- 4.6 175- 554
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Video Segment VVideo Segment V
• Automatically Bounding Geometric Automatically Bounding Geometric Complexity by Using ImagesComplexity by Using Images– Aliaga99Aliaga99
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Cells and PortalsCells and Portals
[Airey90, Teller91, Luebke95][Airey90, Teller91, Luebke95]
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Portal ImagesPortal Images
[Aliaga97][Aliaga97]
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Creating Portal ImagesCreating Portal Images
portaleye
Ideal portal Ideal portal image would be image would be one sampled one sampled from the current from the current eye positioneye position
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Creating Portal ImagesCreating Portal Images
Reference COPs
Display one of a large number of pre-computed images (~120) portal
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Creating Portal ImagesCreating Portal Images
portal
Reference COPs
or…
Warp one of a much smaller number of reference images
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Brooks HouseBrooks House
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 50 100 150 200
Frame
Ren
der
ing
Tim
e (s
ec.)
View Frustum Culling
Portal Culling
Static Portal Images
Onyx IR
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Brooks HouseBrooks House
0
0.1
0.2
0.3
0.4
0.5
0 100 200 300 400 500
Frame
Re
nd
erin
g T
ime
(sec
.)
Portal Culling
Warped Portal Images
Onyx IR
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Video Segment VIVideo Segment VI
• Architectural Walkthroughs using Architectural Walkthroughs using Portal ImagesPortal Images– Aliaga97, Rafferty98Aliaga97, Rafferty98
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OverviewOverview
• Replacing Geometry with ImagesReplacing Geometry with Images• Displaying ImagesDisplaying Images
– Texture-mapping and error metricsTexture-mapping and error metrics– Geometry and image warping Geometry and image warping – Meshes, Lightfield/LumigraphMeshes, Lightfield/Lumigraph
• Image PlacementImage Placement– Automatically Bounding Model ComplexityAutomatically Bounding Model Complexity– Cells and PortalsCells and Portals
• ConclusionsConclusions
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Image QualityImage Quality
• What about measuring quality? What about measuring quality? – Need a perceptual quality metric!Need a perceptual quality metric!
• We knowWe know– Texture-mapping: bad perspective, Texture-mapping: bad perspective,
small distortions believable (geometry small distortions believable (geometry warping)warping)
– IBR: correct perspective, disocclusionsIBR: correct perspective, disocclusions– Meshes: stretching of skinsMeshes: stretching of skins
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LimitationsLimitations
• Diffuse illuminationDiffuse illumination– Deferred shading?Deferred shading?
• Static modelsStatic models– Incremental updating?Incremental updating?
• Cannot sample all visible surfacesCannot sample all visible surfaces– Smarter reconstruction/resampling?Smarter reconstruction/resampling?
• Can only sample surfaces at a fixed Can only sample surfaces at a fixed resolutionsresolutions– Multi-resolution reference images?Multi-resolution reference images?
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AcknowledgmentsAcknowledgments
• Authors of the Video SegmentsAuthors of the Video Segments• ModelsModels
– Discreet Logic, UNC Walkthrough Discreet Logic, UNC Walkthrough GroupGroup
• UNC-Chapel HillUNC-Chapel Hill– Walkthrough, PixelFlow, ImageFlowWalkthrough, PixelFlow, ImageFlow
• NSF, NIH, DARPANSF, NIH, DARPA• Lucent Technologies Bell LabsLucent Technologies Bell Labs