Frédo Durand, George Drettakis, Joëlle Thollot and Claude Puech
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1 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 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] viewpoint Potentially visible trivially occluded
6 Occlusion culling - Principle Quickly reject hidden geometry Z-buffer for final visibility viewpoint Potentially visible Z-buffer
7 Occlusion culling - Problem 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 portal visible [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 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 occluder occludee 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 cell
34 Occludee Projection Reduced to two 2D problems Supporting/separating lines cell
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 1 group 2 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 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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