Transcript Visibility Culling III: Image-Space Occlusion David Luebke Computer Science Department

Visibility Culling III:
Image-Space Occlusion
David Luebke
Computer Science Department
University of Virginia
<[email protected]>
Review:
General Occlusion Culling
• When cells and portals don’t work…
– Trees in a forest
– A crowded train station
• Need general occlusion culling
algorithms:
– Aggregate occlusion
– Dynamic scenes
– Non-polygonal scenes
D2
Review:
Hierarchical Z-Buffer
• A Z-pyramid represents depth buffer
in hierarchical fashion
– Depth at each pixel at a given level
represents the max of the depth of the
four corresponding pixels at next level
– Rasterize triangles into buffer
hierarchically with early termination
D3
Review:
Hierarchical Z-Buffer
• Organize scene into an octree (a kind
of spatial hierarchy)
– Before rendering the polygons
associated with a node in the octree,
test if the node’s bbox is visible
– Do an occlusion query to “test render”
the faces of the bounding box against
the depth-buffer
– If query fails, bbox would not be visible
and we can skip the node’s geometry
D4
Review:
Hierarchical Z-Buffer
• HZB algorithm is most effective when
Z-pyramid is “mostly complete”
– Start by rendering nodes visible last frame
directly into full-res Z-buffer
– Build Z-pyramid from full-res Z-buffer
– Traverse octree front-to-back, using
Z-query to test nodes and render if needed
• Note: also test nodes already rendered to see if they
have become invisible
D5
Review:
Hierarchical Z-Buffer
• HZB algorithm exploits:
– Image-space coherence:
• Polygons occluded in one pixel are probably
occluded in nearby pixels
– Object-space coherence:
• Polygons near an occluded polygon are
probably also occluded
– Temporal coherence:
• Polygons visible recently are probably still
visible
D6
Hierarchical
Occlusion Maps
• A more hardware-friendly general
occlusion culling algorithm
• Two major differences from HZB:
– Separates occluders from occludees
– Decouples occlusion test into an depth
test and a overlap test
D7
Hierarchical
Occlusion Maps
• Occluders versus occludees:
Blue parts: occluders
Red parts: occludees
D8
Hierarchical
Occlusion Maps
• Depth versus overlap:
View
Point
X
Z
Y
Depth
+
Overlap
= Occlusion
D9
Hierarchical
Occlusion Maps
• Representation of projection for
overlap test: occlusion map
– Corresponds to a screen subdivision
– Records average opacity per partition
• Generate by rendering occluders
– Record pixel opacities (i.e., coverage)
D10
Occlusion Maps
Rendered Image
Occlusion Map
D11
Occlusion Map Pyramid
• Analyzing cumulative projection:
– A hierarchical occlusion map (HOM)
– Generate by recursive averaging (once
per frame)
– Records average opacities for blocks of
multiple pixels, representing occlusion
at multiple resolutions
– Construction can be accelerated by
texture hardware
D12
Occlusion Map Pyramid
64 x 64
32 x 32
16 x 16
D13
Occlusion Map Pyramid
D14
Overlap Tests
• Query: is projection of occludee inside
cumulative projection of occluders?
– Cumulative projection: occlusion
pyramid
– Occludee projection: expensive in general
• Overestimate occludee with 3-D bounding box
• Overestimate projection of 3-D bounding box
with 2-D bounding rectangle in screen-space
D15
Overlap Tests
• Hierarchical structure enables some
optimizations:
– Predictive rejection
• Terminate test when it must fail later
– Conservative rejection
• The transparency threshold
– Aggressive Approximate Culling
• Ignore objects barely visible through holes
• The opacity threshold
D16
Aggressive
Approximate Culling
0
1
2
3
4
D17
Hierarchical
Occlusion Maps
• Not discussed here:
– Depth test
• Depth estimation buffer
• Modified Z-buffer
– Selecting occluders
• For more details, see Hansong
Zhang’s dissertation at UNC
D18
HOM: Discussion
• Provides a robust, general, hardwarefriendly occlusion culling algorithm
– Supports dynamic scenes
– Supports non-polygonal geometry
– Few hardware assumptions
D19
HOM: Discussion
• Not clear how relevant the actual
algorithm is on modern hardware
• Most interesting/important points:
– Separation of occluders and occludees
– Factoring occlusion into overlap + depth
– Aggressive approximate culling
• To think about:
– How could we simplify the occluders to
accelerate occlusion testing?
D20
Current
Hardware Support
• Recent hardware supports
Z-query operation
– Allows systems to exploit:
• Object-space coherence (bounding boxes)
• Temporal coherence (last-rendered list)
– Examples in OpenGL:
• HP_OCCLUSION_QUERY
• NV_OCCLUSION_QUERY
• Go to NVIDIA occlusion presentation…
– An aside: applies to cell-portal culling!
D21
Visibility Culling:
Discussion
• When is visibility culling worthwhile?
– When scene has high depth complexity
• Examples: architectural walkthroughs, complex
CAD assemblies, dense forest
• Non-examples: terrain (usually), single highlytessellated object (e.g., bunny, a radiositized
room)
D22
Visibility Culling:
Discussion
• How does visibility culling compare to:
– Level-of-detail:
• Reduces geometry processing
• Helps transform-bound apps
– Visibility culling:
• Reduces geometry and pixel processing
• Helps transform- and fill rate-bound apps
– Texture / Image representations:
• Reduces geometry and pixel processing
• Incurs texture/image processing costs
D23
Visibility Culling:
Discussion
• How does visibility culling interact
with level of detail?
– Fairly seamless integration; generally a
win
– One issue: visibility of simplified
model may differ from original model;
requires some care
– LODs can speed up occluder selection
and rendering
D24
Visibility Culling:
Discussion
• How does visibility culling interact with
texture and image-based representations?
– Texture/image reps generally replace far-field
geometry
• Involves an implicit occlusion culling step
• Reduces scene depth complexity, decreasing the utility of
visibility culling
• If near-field geometry still includes complex heavilyocclusive assemblies, still a win
D25
Visibility Culling:
Discussion
• How much culling effort is appropriate?
– Cells and portals: relatively cheap, with large
potential speedups
– Hierarchical occlusion maps: relatively costly,
carefully weigh potential gains
– Multiple processors allow much more aggressive
culling calculation
• Pipelining culling calculations, Performer-style, allows
cull time = render time
• Tradeoff: one frame increased latency
D26
Summary
• The basic, very powerful idea:
– Rapidly compute a potentially visible set
– Let hardware handle the rest
• For many scenes, visibility culling is
a simple way to get huge speedups
– View-frustum culling always a must
– For scenes with high depth complexity,
occlusion culling can be a big win
D27
Summary
• Architectural models: visibility is
practically a solved problem
– Cells and portals work well
• Cull-box portal culling: simple, fast
• Line-stabbing: elegant, powerful
D28
Summary
• Occlusion culling of general models:
still a largely open problem
– Important issues:
• Dynamic scenes
• Aggregate occlusion effects
– Image-based approaches seem most
promising at this time
D29
Summary
• General occlusion culling algorithms:
– Hierarchical Z-buffer:
• A simple, truly elegant algorithm
• But doesn’t seem amenable to hardware
– Hierarchical occlusion maps:
• Separates occluders from occludees, overlap from depth
• Lends itself well to aggressive culling
• Fairly high overhead, only worthwhile with high depth
complexity
– Recent hardware supports (asynchronous)
occlusion query
D30