Frédo Durand, George Drettakis, Joëlle Thollot and Claude ...
Transcript of Frédo Durand, George Drettakis, Joëlle Thollot and Claude ...
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Frédo Durand, George Drettakis,Joëlle Thollot and Claude Puech
iMAGIS-GRAVIR/IMAG-INRIA (Grenoble, France)Laboratory for Computer Science – MIT (USA)
Frédo Durand, George Drettakis,Joëlle Thollot and Claude Puech
iMAGIS-GRAVIR/IMAG-INRIA (Grenoble, France)Laboratory for Computer Science – MIT (USA)
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Special thanks • Leo Guibas• Mark de Berg
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Introduction• Walkthrough of large models
– Simulators, games, CAD/CAM, urban planning– Millions of polygons– Not real-time with current graphics hardware
• Acceleration– Geometric Levels of Detail (LOD)– Image-based simplification (impostors)– View Frustum culling– Occlusion-culling
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Occlusion culling - Principle• Quickly reject hidden geometry• [Jones 71, Clark 1976]
viewpoint
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Occlusion culling - Principle• Quickly reject hidden geometry• [Jones 71, Clark 1976]
viewpoint
“trivially”“trivially”occludedoccludedPotentially
visible
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Occlusion culling - Principle• Quickly reject hidden geometry• Z-buffer for final visibility
viewpoint Z-bufferPotentially
visible
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Occlusion culling - Problem
object
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• Online point-based / Preprocessing (cells)
Occlusion culling - Classification
[Greene 93, Coorg 96, Zhang 97, Luebke 95, etc.]
[Teller 91, Airey 91, Cohen-Or 98, etc.]
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• Online point-based / Preprocessing (cells)
Occlusion culling - Classification
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• Online point-based / Preprocessing (cells)
• Occluders / Portals
Occlusion culling - Classification
[Greene 93, Coorg 96, Zhang 97, Cohen-Or 98, etc.]
[Teller 91, Airey 91, Luebke 95, etc.]
portaloccluder visiblehidden
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• Online point-based / Preprocessing (cells)
• Occluders / Portals
Occlusion culling - Classification
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• Online point-based / Preprocessing (cells)
• Occluders / Portals
• Object space / Image space
Occlusion culling - Classification
[Teller 91, Airey 91, Coorg 96, Hudson 97, Cohen-Or 98, etc.]
[Greene 93, Zhang 97, etc.]
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• Online point-based / Preprocessing (cells)
• Occluders / Portals
• Object space / Image space
Occlusion culling - Classification
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• Online point-based / Preprocessing (cells)
• Occluders / Portals
• Object space / Image space
Occlusion culling - Classification
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Occlusion culling - Classification• Online point-based / Preprocessing (cells)
• Occluders / Portals
• Object space / Image space
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Occlusion culling - Classification• Online point-based / Preprocessing (cells)
• Occluders / Portals
• Object space / Image space
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Our approach• Visibility preprocess
– Objects invisible from a volumetric cell
• Conservative computation– Do not declare a visible object hidden
• Occluder fusion– Occlusion by multiple rather than single occluder(s)
• Extension of image-spacepoint-based occlusion culling
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Very related work - Fuzzy visibility• Similar initial idea as ours• Unfortunately unknown to us for final version
• [Toward a Fuzzy Hidden Surface Algorithm.Hong Lip LimComputer Graphics International, Tokyo, 1992]
• Read the updated version of our paperhttp://graphics.lcs.mit.edu/~fredo
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On-line point-based occlusion culling• [Greene et al. 93, Zhang et al. 97]
occluder
viewpoint
occludee
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On-line point-based occlusion culling
occludee
occluder
viewpoint
• [Greene et al. 93, Zhang et al. 97]– Projection from a point– Overlap and depth test
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Extended projections• Projection from a point volume• Overlap and depth test
occluder
cell
occludee
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Extended projections• Projection from a point volume• Overlap and depth test• Fixed plane 3D position• Will be discussed
occluder
cell
occludee
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Extended projections• Conservative
– Underestimate the occluders– Overestimate the occludees
cell
occluder
occludee
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Extended projections• Conservative
– Intersection for the occluders
occluder
cell
occludee
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Extended projections• Conservative
– Intersection for the occluders– Union for the occludees
occluder
cell
occludee
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Extended projections• Conservative
– Underestimate the occluders– Overestimate the occludees
occluder
cell
occludee
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Occluder fusion• Two occluders, one occludee
B
cell
A
occludee
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Occluder fusion• Projection of the first occluder
B
cell
A
occludee
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Occluder fusion• Projection of the second occluder• Aggregation in a pixel-map
B
cell
A
occludee
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Occluder fusion• Test of the occludee• The occlusion due to
the combinationof A and B is treated
B
cell
A
occludee
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Fuzzy visibility[Lim 1992]• Extended projection as a fuzzy analysis• Same definition with unions/intersections• However, plane at infinity (direction space)
– Thus works only for infinite umbra
• Concave mesh projection
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Our new method• New Projection algorithms• Heuristic for choice of projection plane• Reprojection• Occlusion sweep• Improved projection• Occlusion culling system
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Occludee Projection
cell
occludee
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Occludee Projection• Reduced to two 2D problems• Supporting/separating
lines
cell
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Convex occluder Projection• Convex cell =>
intersection of views from vertices of the cell• Hardware computation using the stencil buffer• Conservative rasterization
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Concave occluder slicing• Intersection
occluder-projection plane
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Difficulty of choosing the plane• First possible plane• Fine
occluder
cell
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Difficulty of choosing the plane• Other possible plane• The intersection of the views is null
occluder
cell
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• Heuristic (maximize projected surface)• Works fine for most cases (e.g. city)
Choosing the plane
occluder
cell
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Problem of the choice of the plane• Contradictory constraints
group 1
cell
group 2
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Solution• Project on plane 1
– Aggregate extended projections
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Re-projection• Re-project aggregated occlusion map onto plane 2 • Convolution [Soler 98]
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Occlusion sweep• Initial projection plane
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Occlusion sweep• Re-projection• Projection of new occluders
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Occlusion sweep• Re-projection• Projection of new occluders
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Occlusion sweep• Re-projection• Projection of new occluders
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Improved Extended Projection• Detect more occlusion for some configurations• For convex and planar occluders• Do not use unions for occludees
(supporting lines only)
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Adaptive preprocessing• If cell has too many visible objects
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Adaptive preprocessing• If cell has too many visible objects
then subdivide
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Interactive viewer• Potentially Visible Set precomputation• Visibility flag in the object hierarchy• No cost at runtime• Moving objects: motion volume
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Results - Single projection plane• City scene (6 million polygons)• 165 minutes of preprocess (0.81 seconds per cell)• 18 times speedup wrt view frustum culling• Informal comparison with [Cohen-Or et al. 98]
(no occluder fusion, single occluder):– 4 times fewer
remaining objects– 150 times faster
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Video
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Video
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Results – Occlusion sweep• Forest scene (7.8 million polygons)• 15 plane positions• 23 seconds per cell• 24 times speedup wrt view frustum culling
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Video
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Video
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Discussion- More remaining objects than on-line methods- No moving occluders
+ Occluder fusion+ No cost at display time+ Prediction capability
scenes which do not fit into main memorypre-fetching (network, disk)
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Future work• Better concave occluder Projection
– e.g. adaptation of [Lim 1992]
• On-demand computation• Application to global illumination• Use with other acceleration methods
– LOD or image-based acceleration– Driven by semi-quantitative visibility– Take perceptual masking into account