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Image Space Occlusion Culling Image Space Occlusion Culling 1 - - PowerPoint PPT Presentation
Image Space Occlusion Culling Image Space Occlusion Culling 1 - - PowerPoint PPT Presentation
Image Space Occlusion Culling Image Space Occlusion Culling 1 Hudson et al, SoCG 97 Occluder A Viewpoint umbra B C 2 What Methods are Called Image-Space? Those where the decision to cull or render is done after projection (in image
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What Methods are Called Image-Space?
- Those where the decision to cull or render is
done after projection (in image space)
View volume Object space hierarchy Decision to cull
Two classic examples
– Hierarchical Z-Buffer [HBZ93] – Hierarchical Occlusion Maps [HOM97]
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Ingredients of an Image Space Method
- An object space data structure that
allows fast queries to the complex geometry
Space partitioning Hierarchical bounding boxes Regular grid
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An image space representation of the
- cclusion information
- Discrete
– Z-hierarchy – Occlusion map hierarchy
- Continuous
– BSP tree – Image space extends
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General Outline of Image Space Methods
- During the (front-to-back) traversal of the
scene hierarchy do:
– compare each node against the view volume – if not culled, test node for occlusion – if still not culled, render objects/occluders augmenting the image space occlusion
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Testing a Node for Occlusion
- If the box representing a node is not visible then
nothing in it is either
- The faces of the box are projected onto the image
plane and tested for occlusion
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Testing a Node for Occlusion
- If the box representing a node is not visible then
nothing in it is either
- The faces of the box are projected onto the image
plane and tested for occlusion
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Hierarchical Tests
O
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Hierarchical Tests
O
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Hierarchical Tests
O
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Differences of Algorithms
- The most important differences between
the various approaches are:
– the representation of the (augmented)
- cclusion in image space and,
– the method of testing the hierarchy for
- cclusion
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Hierarchical Z-Buffer (HZB)
(Ned Greene, Michael Kass 93)
- An extension of the Z-buffer VSD algorithm
- It follows the outline described above.
- Scene is arranged into an octree which is
traversed top-to-bottom and front-to-back.
- During rendering an occlusion map is
incrementally built.
- Octree nodes are compared against
- cclusion map.
- The occlusion map is a z-pyramid…
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The Z-Pyramid
Objects are rendered Depth taken from the z-buffer Construct pyramid by taking max of each 4
= furthest = closer = closest
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Maintaining the Z-Pyramid
- Ideally every time an object is rendered
causing a change in the Z-buffer, this change is propagated through the pyramid
- However this is not a practical approach
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More Realistic Implementation
- Make use of frame-to-frame coherence:
– at start of each frame render the nodes that were visible in previous frame – read the z-buffer and construct the z-pyramid – now traverse the octree using the z-pyramid for occlusion but without updating it
Cool idea!
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HZB: discussion
- It provides good acceleration in very
dense scenes
- Getting the necessary information from
the Z-buffer is costly
- A hardware modification was proposed
for making it real-time
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Hierarchical Occlusion Maps
(Hansong Zhang et.al 97)
Similar idea to HZB but:
– they separate the coverage information from the depth information, two data structures
- hierarchical occlusion maps
- depth (several proposals for this)
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HOM:Algorithm Outline
– Select occluders until the set is large enough – Build occlusion representation – Occlusion culling & final rendering
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Demonstration
Blue parts: Blue parts: occluders
- ccluders
Red parts: Red parts: occludees
- ccludees
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Occlusion Map Pyramid
64 x 64 32 x 32 16 x 16
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Occlusion Map Pyramid
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Set of Occluders Occlusion Map
Representing Occluders
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