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Conservative Visibility Preprocessing using Extended Projections Frédo Durand, George Drettakis, Joëlle Thollot and Claude Puech iMAGIS-GRAVIR/IMAG-INRIA.

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Presentation on theme: "Conservative Visibility Preprocessing using Extended Projections Frédo Durand, George Drettakis, Joëlle Thollot and Claude Puech iMAGIS-GRAVIR/IMAG-INRIA."— Presentation transcript:

1 Conservative Visibility Preprocessing using Extended Projections Frédo Durand, George Drettakis, Joëlle Thollot and Claude Puech iMAGIS-GRAVIR/IMAG-INRIA (Grenoble, France) Laboratory for Computer Science – MIT (USA)

2 Special thanks Leo Guibas Mark de Berg

3 Introduction Walkthrough of large models Acceleration
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

4 Occlusion culling - Principle
Quickly reject hidden geometry [Jones 71, Clark 1976] viewpoint

5 Occlusion culling - Principle
Quickly reject hidden geometry [Jones 71, Clark 1976] “trivially” occluded Potentially visible viewpoint

6 Occlusion culling - Principle
Quickly reject hidden geometry Z-buffer for final visibility Potentially visible Z-buffer viewpoint

7 Occlusion culling - Problem
How can we detect the “trivially” occluded objects? object

8 Occlusion culling - Classification
Online point-based / Preprocessing (cells) [Greene 93, Coorg 96, Zhang 97, Luebke 95, etc.] [Teller 91, Airey 91, Cohen-Or 98, etc.]

9 Occlusion culling - Classification
Online point-based / Preprocessing (cells)

10 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals occluder hidden visible portal [Greene 93, Coorg 96, Zhang 97, Cohen-Or 98, etc.] [Teller 91, Airey 91, Luebke 95, etc.]

11 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals

12 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals Object space / Image space [Teller 91, Airey 91, Coorg 96, Hudson 97, Cohen-Or 98, etc.] [Greene 93, Zhang 97, etc.]

13 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals Object space / Image space

14 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals Object space / Image space

15 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals Object space / Image space

16 Occlusion culling - Classification
Online point-based / Preprocessing (cells) Occluders / Portals Object space / Image space

17 Our approach Visibility preprocess Conservative computation
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-space point-based occlusion culling

18 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 Lim Computer Graphics International, Tokyo, 1992] Read the updated version of our paper

19 On-line point-based occlusion culling
[Greene et al. 93, Zhang et al. 97] occludee occluder viewpoint

20 On-line point-based occlusion culling
[Greene et al. 93, Zhang et al. 97] Projection from a point Overlap and depth test occludee occluder viewpoint

21 Extended projections Projection from a point volume
Overlap and depth test occludee occluder cell

22 Extended projections Projection from a point volume
Overlap and depth test Fixed plane 3D position Will be discussed occludee occluder cell

23 Extended projections Conservative Underestimate the occluders
Overestimate the occludees occludee occluder cell

24 Extended projections Conservative Intersection for the occluders
occludee occluder cell

25 Extended projections Conservative Intersection for the occluders
Union for the occludees occludee occluder cell

26 Extended projections Conservative Underestimate the occluders
Overestimate the occludees occludee occluder cell

27 Occluder fusion Two occluders, one occludee occludee A B cell

28 Occluder fusion Projection of the first occluder occludee A B cell

29 Occluder fusion Projection of the second occluder
Aggregation in a pixel-map occludee A B cell

30 Occluder fusion Test of the occludee
The occlusion due to the combination of A and B is treated occludee A B cell

31 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

32 Our new method New Projection algorithms
Heuristic for choice of projection plane Reprojection Occlusion sweep Improved projection Occlusion culling system

33 Occludee Projection occludee

34 Occludee Projection Reduced to two 2D problems
Supporting/separating lines

35 Convex occluder Projection
Convex cell => intersection of views from vertices of the cell Hardware computation using the stencil buffer Conservative rasterization

36 Concave occluder slicing
Intersection occluder-projection plane

37 Difficulty of choosing the plane
First possible plane Fine occluder cell

38 Difficulty of choosing the plane
Other possible plane The intersection of the views is null occluder cell

39 Choosing the plane Heuristic (maximize projected surface)
Works fine for most cases (e.g. city) occluder cell

40 Problem of the choice of the plane
Contradictory constraints group 2 group 1 cell

41 Solution Project on plane 1 Aggregate extended projections

42 Re-projection Re-project aggregated occlusion map onto plane 2
Convolution [Soler 98]

43 Occlusion sweep Initial projection plane

44 Occlusion sweep Re-projection Projection of new occluders

45 Occlusion sweep Re-projection Projection of new occluders

46 Occlusion sweep Re-projection Projection of new occluders

47 Improved Extended Projection
Detect more occlusion for some configurations For convex and planar occluders Do not use unions for occludees (supporting lines only)

48 Adaptive preprocessing
If cell has too many visible objects

49 Adaptive preprocessing
If cell has too many visible objects then subdivide

50 Interactive viewer Potentially Visible Set precomputation
Visibility flag in the object hierarchy No cost at runtime Moving objects: motion volume

51 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

52 Video

53 Video

54 Results – Occlusion sweep
Forest scene (7.8 million polygons) 15 plane positions 23 seconds per cell 24 times speedup wrt view frustum culling

55 Video

56 Video

57 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 memory pre-fetching (network, disk)

58 Future work Better concave occluder Projection On-demand computation
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

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