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Visibility Culling. Roger A. Crawfis CIS 781 The Ohio State University. Interactive Frame Rates Are Difficult To Achieve. The Problem. Two keys for an interactive system Interactive rendering speed: too many polygons – difficult!!

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Visibility culling

Visibility Culling

Roger A. Crawfis

CIS 781

The Ohio State University


Interactive frame rates are difficult to achieve

Interactive Frame Rates Are Difficult To Achieve


The problem

The Problem

  • Two keys for an interactive system

    • Interactive rendering speed: too many polygons – difficult!!

    • Uniform frame rate: varied scene complexity – difficult!!


Possible solutions

Possible Solutions

  • Visibility Culling – back face culling, frustum culling, occlusion culling (might not be sufficient)

  • Levels of Detail (LOD) – hierarchical structures and choose one to satisfy the frame rate requirement


Lod selections

LOD Selections

How to pick the

Optimal ones??!!


Occlusion culling

Occlusion Culling

  • Hidden Surface Removal methods are not fast enough for massive models on current hardware

  • Occlusion Culling avoids rendering primitives that are occluded by another part of the scene

  • Occlusion Culling techniques are ideally output sensitive – runtime is proportional to the size of exact visibility set


Related work

Related Work

  • Hierarchical Z-Buffer

    • Image space occlusion culling method [Greene’93]

    • Build a layered Z-pyramid with a different resolution of the Z-buffer at each level

    • Allows quick accept/reject

  • Hierarchical LODs

    • Simplification Culling : Approximate entire branch of the scene graph by an HLOD

    • Can we use HLODs as occluders/occludees?


Visibility in games

Visibility in Games

  • What do we need it for?

    • Increase of rendering speed by removing unseen scene data from the rendering pipeline as early as possible

    • Reduction of data transfers to the graphics hardware

    • Current games would not be possible without visibility calculations


Visibility methods

Visibility methods

  • 2 very different categories:

    • Visibility from a region (Portals, PVS)

      • (Quake, Unreal, Severance and co.)

    • Visibility from a point (Z-Buffer, BFC,...)

      • Racing games, outdoor scenes, sports games etc.


Point visibility occlusion

Point-Visibility Occlusion

  • Traditionally used:

    • Back-Face culling

    • Z-Buffering

    • View frustum culling

      • Octree

      • Quadtree


A psx example

A PSX Example

  • Iron Soldier 3 on PSX:

    • View frustum culling based on a quad-tree

    • Back-face culling

    • Painters algorithm

Only culling to the leftand right sides of theviewing frustum.


New occlusion methods

New Occlusion Methods

  • Image-space occlusion culling

    • Hierarchical Z-Buffering

    • Hierarchical Occlusion Maps

  • Object-space occlusion culling

    • Hierarchical View Frustum culling

    • Hierarchical Back-Face culling


Visibility culling1

Visibility Culling

  • We will look at these:

    • Hierarchical Back-face culling

    • View-frustum culling

    • Occlusion culling

    • Detail culling


Hierarchical back face culling

Hierarchical Back-Face Culling

  • Partitions each model into clusters

  • Primitives in one cluster are:

    • Facing into similar directions

    • Lie close to each other

  • If the cluster fails the visibility test, all primitives in this cluster are culled


Hierarchical back face culling1

Hierarchical Back-Face Culling


Normal maps

Normal Maps

  • Create a data structure that places each polygon in the space according to its normal direction.

  • Partition this space and then simply look at those partitions that might have visible polygons.

phi

theta


View frustum culling

  • Construct bounding

  • volumes (BVs)

  • Create hierarchy

  • BV/V-F intersection

  • tests

View-Frustum Culling

  • Remove objects that are outside the viewing frustum

Mostly done in “Application Stage”


View frustum culling1

View-Frustum Culling

  • Culling against bounding volumes to save time

  • Bounding volumes – AABB, OBB, Spheres, etc. – easy to compute, as tight as possible

Sphere

OBB

AABB


View frustum culling2

View-Frustum Culling

  • Often done hierarchically to save time

In-order, top-down

traversal and test


View frustum culling3

View-Frustum Culling

  • Two popular hierarchical data structures – BSP Tree and Octree

Axis-Aligned BSP

Polygon-Aligned BSP

Intersecting?


View frustum culling4

View-Frustum Culling

  • Octree

  • A parent has 8 childrens

  • Subdivide the space until the

  • number of primitives within

  • each leaf node is less than a

  • threshold

  • In-order, top-down traversal


Hierarchical z buffer

Hierarchical Z-Buffer

  • Z-Buffer is arranged in an image pyramid.

  • Scene is partitioned in an octree.

  • Octree nodes are tested against the Z-Pyramid where pixels have the same size.

  • Visible nodes serve as input for the next frame.

  • Relies on HW visibility query.


Hzb hierarchical occlusion maps

HZB/Hierarchical occlusion maps


Hierarchical occlusion maps

Hierarchical occlusion maps

  • Potential occluders are pre-selected

  • These occluders are rendered to the occlusion map. The hierarchy can be built with MIP-Mapping HW

  • Depth test after occlusion test

  • Separate depth estimation buffer


Hierarchical view frustum culling

Hierarchical View Frustum Culling

  • Speeds up VFC by testing only 2 box corners of a bounding box first.

  • Plane coherency during frame advancing

  • Test against VF-octants.

  • BB-Child masking


Detail culling

Detail Culling

  • A technique that sacrifices quality for speed

  • Base on the size of projected BV – if it is too small, discard it.

  • Also often done hierarchically.

Always helps to create a hierarchical

structure, or scene graph.


Occlusion culling1

Occlusion Culling

  • Discard objects that are occluded

  • Z-buffer is not the smartest algorithm in the world (particularly for high depth-

    complexity scenes)

  • We want to avoid the processing of invisible objects


Occlusion culling2

Occlusion Culling

  • G: input graphics data

  • Or: occlusion representation

  • The problem:

  • algorithms for isOccluded()

  • Fast update Or

OcclusionCulling (G)

Or = empty

For each object g in G

if (isOccluded(g, Or))

skip g

else

render (g)

update (Or)

end

End


Hierarchical visibility

Hierarchical Visibility

  • Object-space octree

    • Primitives in a octree node are hidden if the octree node (cube) is hidden

    • A octree cube is hidden if its 6 faces are hidden polygons

    • Hierarchical visibility test:


Hierarchical visibility obj sp

Hierarchical Visibility (obj-sp.)

From the root of octree:

  • View-frustum culling

  • Scan conversion each of the 6 faces and perform z-buffering

  • If all 6 faces are hidden, discard the entire node and sub-branches

  • Otherwise, render the primitives here and traverse the front-to-back children recursively

A conservative algorithm – why?


Hierarchical visibility obj sp1

Hierarchical Visibility (obj-sp.)

  • Scan conversion the octree faces can be expensive – cover a large number of pixels (overhead)

  • How can we reduce the overhead?

  • Goal: quickly conclude that a large polygon is hidden

  • Method: use hierarchical z-buffer !


Hierarchical z buffer1

Hierarchical Z-buffer

An image-space approach

  • Create a Z-pyramid

1 value

¼ resolution

½ resolution

Original Z-buffer


Hierarchical z buffer 2

  • 1 0 6

0 3 1 2

  • 6

9

3 9 1 2

9 2

9 1 2 2

Hierarchical Z-buffer (2)

Keep the maximum value


Hierarchical z buffer2

Hierarchical Z-buffer

update

Visibility (OctreeNode N)

if (isOccluded (N, Zp) then return;

for each primitive p in N

render and update Zp

end

for each child node C of N in front-to-back order

Visibility ( C )

end


Some practical issues

Some Practical Issues

  • A fast software algorithm

  • Lack of hardware support

    • Scan conversion

    • Efficient query of if a polygon is visible (without render it)

    • Z feedback


Combining with hardware

Combining with hardware

  • Utilizing frame-to-frame coherence

    • First frame – regular HZ algorithm (software)

      • Remember the visible octree nodes

    • Second frame (view changes slightly)

      • Render the previous visible nodes using OpenGL

      • Read back the Z-buffer and construct Z-pyramid

      • Perform regular HZ (software)

    • What about the third frame?

    • Utilizing hardware to perform rendering and Z-buffering – considerably faster


Hierarchical occlusion map

Hierarchical Occlusion Map

Zhang et al

SIGGRAPH 98


Basic ideas

Basic Ideas

  • Choose a set of graphics objects from the scene as Occluders

  • Use the occluders to define an Occlusion Map (hierarchically)

  • Compare the rest of scene against the occlusion map


Example

Example

Blue: Occluders

Red: Occludees


Algorithm pipeline

Occluder Viewing Frustum Occluder Rendering

Database Culling Selection

Build Occlusion

Map Hierarchy

Real Viewing Frustum Occlusion Test

Scene Culling

Algorithm Pipeline


2 step occlusion test

2-Step Occlusion Test

  • Overlap Test

  • Overlap Test

Overlap + Depth = Occlusion


Why decomposition

Why decomposition?

  • The occlusion test is done approximately (conservatively)

  • We can afford to be more conservative in depth test than overlap test


Why decomposition1

Why Decomposition?


Overlap test occlusion map

Overlap Test – Occlusion Map

  • Representation of projection for overlap test: occlusion map

  • A gray scale image – each pixel represents one block of screen region

  • Generate by rendering occluders


Occlusion map om

Occlusion Map (OM)

  • Each pixel of the occlusion map has an opacity, which represents the ratio of the sum of the opaque areas in the block to the total area.

  • If fully covered, p= 1, if anti-alised pixel, p <1)

  • Occlusion map: the alpha channel of an image


Overlap test using om

Overlap Test using OM

For each potential occludee, we can scan-convert

it and compare against the opacity of the pixels it

overlaps Expensive!!

  • Conservative Approximation: use the screen-space

  • bounding box of the occludee (a superset of the actual

  • covered pixels)

  • If all the pixels inside the bounding box are opaque,

  • the object is occluded.


Hierarchical occlusion map1

Hierarchical Occlusion Map

Like hierarchical Z-buffer, we can create a hierachy to

speed up the comparison (for large objects)

The low resolution pixel is

an average of the high

resolution pixels


Overlap test using hom

Overlap Test using HOM

Basic Algorithm

  • Start from the lowest resolution

  • If the pixel cover the bounding

  • rectangle has a value 1,

  • the object is occluded

  • Otherwise traverse down the

  • hierarchy:

    • If all children =1: occluded

    • If all children =0; not occluded

    • Otherwise, traverse down further


Approximate overlap test

Approximate Overlap Test

  • Instead of concluding an object is occluded only when the bounding box is within pixels with opacity 1, we can use an threshold between [0,1]

  • Early termination in the high level of the hierarchy

  • What does it mean when a block has high opacity but not one?

This is the unique feature of HOM !!


Depth test

Depth Test

Approximate Z (depth) test:

  • A single Z Plane

A single Z plane

to separate the

occluders from

occludees.


Depth test1

Depth Test

  • Break the screen into small regions

  • Build at each frame

  • Instead of using Z-buffer, use

  • the occluder’s bounding

  • volume’s farthest Z

  • Compare each potential

  • occludee’s nearest Z (con-

  • servative test)


Occluder selection

Occluder Selection

Ideal occluder: the visible objects – it’s a joke

View-dependent occluder: too expensive

Solution: Estimate and build an occluder database

Discard objects that do not server as

good occluders


Occluder selection1

Occluder Selection

  • Size: not too small

  • Redundant: detail polygons (clock on the wall)

  • Complexity: Complex polygons are not preferred (why?)

  • Done at run time – sort the occluders in depth, add them in order until reach the polygon count.


Visibility culling

OPS

  • View-independent Occluders

X

Z


Visibility culling

OPS

  • View-dependent Occluders


Occludders

Occludders

  • In practice, use traditional, static LOD’s

    • More restrictive view-independent OPS

    • Well-studied and available

    • Low run-time overhead

    • Shared with final rendering, no extra memory

    • Area-preserving [Erikson 98]


Occluder selection2

Occluder selection

  • At run time

    • Distance-based selection with a polygon budget

    • Temporal coherence

  • Visibility sampling

    • Pre-compute visible objects on a 3-D grid

    • Facilitates run-time selection


Implementation

Implementation

  • A two-pass framework

Occluder

Rendering

LOD

Selection

View

Scene

Frustum

Build Occlusion

Database

Culling

Representation

Occlusion

LOD

Culling


Results

Results

  • The city model


Results1

Results

  • The city model

    • 312,524 polygons

    • Single CPU

    • 5,000 occluder polygons

    • Depth estimation buffer

    • Opacity thresholds 1.0

    • Lighting; display lists; no triangle strips


Results2

Results


Results3

Results


Results4

Results

  • Auxiliary Machine Room (AMR)


Results5

Results

  • AMR

    • 632,252 polygons

    • 3 CPUs

    • 25,000 occluder polygons

    • No-background z-buffer

    • Approximate culling (0.85 for level 64x64)

    • LOD

    • Lighting; display lists; no triangle strips


Results6

Results


Results7

Results


Results8

Results


Results9

Results

  • The power plant model


Results10

Results

  • The power plant model

    • 15 million triangles

    • 3 CPUs

    • Visibility pre-processing on a 20x20 grid (~15min)

    • No-background z-buffer

    • 18,000 occluder polygons

    • opacity thresholds from 0.85 and up

    • LOD


Results11

Results


Conclusion

Conclusion

  • Goals achieved

    • Generality

      • Any model, any occluder

      • Occluder fusion

    • Speed-up

      • Accelerate interactive graphics

    • Ease of implementation

      • Configurability

      • Robustness


Hp hardware occlusion

HP hardware occlusion

  • Extend OpenGL – add an OCCLUSION_MODE

  • The bounding box of an object is scan converted

  • A flag is set if any pixel of the BB faces is visible

  • Only need to read back one flag, instead of the entire frame buffer

  • Tradeoff – valuable rendering time is used to render useless BB faces (need to be used wisely)

  • Reportedly 25%-100% speedup were observed


The real world

The Real World

  • Scientific approaches often too complicated

  • Science often uses models with hundreds of thousands of vertices, games don’t. (LOD)

  • Game developers “pick” ideas from different algorithms

  • Research has impact on hardware design!


Gaming industry

Gaming Industry

  • Parts of the Hierarchical Z-Buffer (HZB) are used sometimes

  • Runtime-LOD is used as input for a simple HZB

  • View Frustum Culling (VFC) is almost always used.

  • Hierarchical Occlusion Maps introduce too much overhead for games, and the z-buffer is there anyway


The real world 3

The Real World (3)

  • PSX-One doesn’t even have a z-buffer

  • ATI’s Radeon has parts of a HZB (Called Hyper-Z)

  • GForce2 only has a z-buffer

  • GForce3 similar to Radeon, but supports HZB visibility query

  • Dreamcasts Power-VR2 works pretty different (Infinite planes)


Conclusions

Conclusions

  • Visibility algorithms are used in many different applications

    • Occlusion culling

    • Shadow calculations

    • Radiosity

    • Volumetric lights

  • All these fields benefit from advances in visibility techniques


Recap

Recap

  • Visibility culling: don’t render what can’t be seen

    • Off-screen: view-frustum culling

    • Z-buffered away: occlusion culling

  • Cells and portals

    • Works well for architectural models

    • Teller: accurate, complex, a bit slow

    • pfPortals: fast, cheap, easy


Hierarchical z buffer3

Hierarchical Z-Buffer

  • Q: What do you think this is?

  • Replace Z-buffer with a Z-pyramid

    • Lowest level: full-resolution Z-buffer

    • Higher levels: each pixel represents what?

      • A: Maximum distance of geometry visible to the four pixels “underneath” it

  • Q: How is this going to help?


Hierarchical z buffer4

Hierarchical Z-Buffer

  • Idea: test polygon against highest level first

    • If polygon is further than distance recorded in pixel, stop--it’s occluded

    • If polygon is closer, recursively check against next lower level

  • Amounts to hierarchical rasterization of the polygon, with early termination

    • Must update higher levels as we go


Hierarchical z buffer5

Hierarchical Z-Buffer

  • Z-pyramid exploits image-space coherence: polygon occluded in one pixel is probably occluded nearby

  • HZB also exploits object-space coherence: polygons near an occluded polygon are probably occluded

  • Q: How might you use object-space coherence?


Hierarchical z buffer6

Hierarchical Z-Buffer

  • Subdivide scene with an octree

  • All geometry in an octree node is contained by a cube

  • Before rendering the contents of a node, “render” the faces of its cube

  • If cube faces are occluded, ignore the entire node

  • Query Z-pyramid to “render” cubes


Hierarchical z buffer7

Hierarchical Z-Buffer

  • Exploit temporal coherence (What?)

  • HZB operates at max efficiency when Z-pyramid is already built

  • Idea: most polygons affecting Z-buffer (“nearest polygons”) are the same from frame to frame

  • So start by rendering the polygons (octree nodes) visible last frame


Hierarchical occlusion maps stolen by dave luebke from the ph d defense presentation of

Hierarchical Occlusion Mapsstolen by Dave Luebke from thePh.D. Defense presentation of:

Hansong Zhang

Department of Computer Science

UNC-Chapel Hill


Visibility culling2

Visibility Culling

  • Discard objects not visible to the viewer

View-frustum culling

Back-face culling

View

View

Frustum

Point

Occlusion culling


Hierarchical occlusion maps overview

Hierarchical Occlusion Maps: Overview

Blue parts: occluders Red parts: occludees


Effective algorithms

Effective Algorithms

  • Generality

    • Arbitrary models

  • Speed-up

    • Significant, fast culling for interactive graphics

  • Portability

    • Few hardware assumptions

    • Robustness


  • Thesis statement

    Thesis Statement

    • By properly decomposing the occlusion-culling problem and efficiently representing occlusion, we can obtain effective algorithms and systems for occlusion culling.


    Observations

    Observations

    • Want to handle cumulative occlusion

    A

    B

    View

    Point


    Observations1

    Observations

    • Want an occlusion representation (OR)

      • Fast to compute

      • Fast to use

    A

    B

    View

    Point


    Observations2

    Observations

    • Progressive occlusion culling

      Initialize OR to null

      for each object

      Occlusion test against OR

      if culled

      Discard object

      else

      Render object

      Update OR


    Observations3

    #passes = #updates

    Observations

    • Multi-pass occlusion culling

      Initialize OR to null; initialize PO to empty

      for each object

      Occlusion test against OR

      If culled

      Discard object

      else

      Render object

      Add object to PO

      if PO is large enough

      Update OR with objects in PO

    The set of potential occluders


    Observations4

    Observations

    • Special case: one-pass occlusion culling

      • Select occluders until PO is large enough

      • Update (build) occlusion representation

      • Occlusion culling & final rendering


    Problem decomposition

    View

    X

    Point

    Y

    Z

    Problem Decomposition

    • Occlusion = depth + overlap


    Problem decomposition1

    Problem Decomposition

    • Verifying occlusion

      • Overlap tests

        • Based on representations for projection

      • Depth tests

        • Based on representations for depth


    Visibility culling

    Occlusion Maps

    Rendered Image

    Occlusion Map


    Occlusion maps

    Occlusion Maps

    • An occlusion map

      • Corresponds to a screen subdivision

      • Records average opacity for each partition

    • Can be generated by rendering occluders

      • Record pixel opacities (pixel coverage)

    • Merge projections of occluders

    • Represent occlusion in image-space


    Occlusion map pyramid

    Occlusion Map Pyramid

    64 x 64

    32 x 32

    16 x 16


    Occlusion map pyramid1

    Occlusion Map Pyramid


    Occlusion map pyramid2

    Occlusion Map Pyramid

    • Analyzing cumulative projection

      • A hierarchy of occlusion maps (HOM)

      • Made by recursive averaging (low-pass filtering)

      • Record average opacities for blocks of pixels

      • Represent occlusion at multiple resolutions

      • Construction accelerated by hardware


    Overlap tests

    Overlap Tests

    • Problem: is the projection of tested object inside the cumulative projection of the occluders?

    • Cumulative projection of occluders: the pyramid

    • Projection of the tested object

      • Conservative overestimation

        • Bounding boxes (BB)

        • Bounding rectangles (BR) of BB’s


    Overlap tests1

    Overlap Tests

    • The basic algorithm

    • Given: HOM pyramid; the object to be tested

    • Compute BR and the initial level in the pyramid

    • for each pixel touched by the BR

    • if pixel is fully opaque

    • continue

    • else

    • if level = 0

    • return FALSE

    • else

    • descend...


    Overlap tests2

    Overlap Tests

    • Evaluating opacity: early termination

      • Conservative rejection

      • Aggressive approximate culling

      • Predictive rejection


    Conservative rejection

    Conservative Rejection

    • A low-opacity pixel does not correspond to many high-opacity pixels at finer levels

    • The transparency threshold

    1

    1

    1

    1

    1

    0.8

    1

    1

    0.9

    0.9

    0.1

    0

    0.2

    0.3

    0

    0


    Aggressive approximate culling

    Aggressive Approximate Culling

    • Ignoring barely-visible objects

      • Small holes in or among objects

      • To ignore the small holes

        • LPF suppresses noise — holes “dissolve”

        • Thresholding: regard “very high” opacity as fully opaque

      • The opacity threshold: the opacity above which a pixel is considered to be fully opaque


    Aggressive approximate culling1

    1

    0

    2

    3

    4

    Aggressive Approximate Culling


    Aggressive approximate culling2

    Aggressive Approximate culling

    • Further descent not necessary when fully opaque

      • Tests terminated before holes are reached

    • Need different opacity thresholds for each level


    Predictive rejection

    Predictive Rejection

    • Terminate the test knowing it must fail later...


    Summary levels of visibility

    Summary: Levels of Visibility

    • The continuum between being visible and non-visible

    Occlusion Maps

    Potential Occludees

    Almost visible

    Almost non-visible

    Almost transparent

    (low opacity)

    Almost opaque

    (high opacity)


    Resolving depth

    B does not occlude any part of A

    Resolving Depth

    • What’s left of the occlusion test?

    “A occludes B” = “A’s projection contains B’s” + ?

    B

    A

    Another interpretation...


    Resolving depth1

    Resolving Depth

    • Depth representations

      • Define a boundary beyond which an object overlapping occluders is definitely occluded

      • Conservative estimates:

        • A single plane

        • Depth estimation buffer

      • No-background z-buffer


    A single plane

    A single plane

    • … at the farthest vertex of the occluders

    Image

    plane

    The plane

    Occluders

    The point with nearest depth

    Viewing direction

    This object passes the depth test

    A


    Depth estimation buffer

    Depth Estimation Buffer

    • Like a low-res depth buffer

      • Uniform subdivision of the screen

      • A plane for each partition

      • Defines the far boundary

      • Updates (i.e. computing depth representation)

        • Occluder bounding rectangle at farthest depth

      • Depth tests

        • Occudee bounding rectangle at nearest depth


    Depth estimation buffer1

    Depth Estimation Buffer

    Transformed view-frustum

    D. E. B.

    Image

    plane

    Bounding rectangle at farthest depth

    Bounding rectangle at nearest depth

    Viewing direction

    B

    Occluders

    A


    Depth estimation buffer2

    Depth Estimation Buffer

    • Trade-off

      • Advantages

        • Removes need for strict depth sorting

        • Speed

        • Portability

      • Disadvantages

        • Conservative far boundary

        • Requires good bounding volumes


    No background z buffer

    No-Background Z-Buffer

    • The z-buffer from occluder rendering...

      • is by itself an full occlusion representation

      • has to be modified to support our depth tests

    • “Removing” background depth values

      • Replace them the “foreground” depth values

    • Captures the near boundary


    No background z buffer1

    No-Background Z-Buffer

    Transformed view-frustum

    Image

    plane

    D. E. B

    Occluders

    N. B. Z

    Viewing direction

    A

    Objects passing the depth tests


    No background z buffer2

    No-Background Z-Buffer

    • Trade-off

      • Advantages

        • Captures the near boundary

        • Less sensitive to bounding boxes

      • Disadvantages

        • Assumes quickly accessible z-buffer

        • Resolution same as occlusion maps (however…)


    Occluder selection3

    Occluder Selection

    • Occlusion-preserving simplification (OPS)

    • Run-time selection

    • Visibility pre-processing


    Visibility culling

    OPS

    • View-independent OPS

    X

    Z


    Visibility culling

    OPS

    • View-dependent OPS


    Visibility culling

    OPS

    • In practice, use traditional, static LOD’s

      • More restrictive view-independent OPS

      • Well-studied and available

      • Low run-time overhead

      • Shared with final rendering, no extra memory

      • Area-preserving [Erikson 98]

    • Conservative OPS (COPS)...


    Occluder selection4

    Occluder selection

    • At run time

      • Distance-based selection with a polygon budget

      • Temporal coherence

    • Visibility sampling

      • Pre-compute visible objects on a 3-D grid

      • Facilitates run-time selection


    Implementation1

    Implementation

    • A two-pass framework

    Occluder

    Rendering

    LOD

    Selection

    View

    Scene

    Frustum

    Build Occlusion

    Database

    Culling

    Representation

    Occlusion

    LOD

    Culling


    Implementation2

    FinalDrawN+1

    OccSelN

    CullN

    CullN+2

    Implementation

    • Pipelining

    OccSelN+1

    OccSelN+2

    OccSelN+3

    OccDrawN

    FinalDrawN

    OccDrawN+1

    FinalDrawN+1

    OccDrawN+2

    CullN+1


    Implementation3

    Implementation

    • Uses bounding volume hierarchy

    • Active layers of the pyramid: 4x4 - 64x64

    • Resolutions

      • Occluder rendering - 256x256

      • D. E. B. - 64x64

    • Test platforms

      • SGI Onyx II, 4 195Mhz R10000, InfiniteReality

      • SGI Onyx I, 4 250MHz R4400, InfiniteReality


    Results12

    Results

    • The city model


    Results13

    Results

    • The city model

      • 312,524 polygons

      • Single CPU

      • 5,000 occluder polygons

      • Depth estimation buffer

      • Opacity thresholds 1.0

      • Lighting; display lists; no triangle strips


    Results14

    Results


    Results15

    Results


    Results16

    Results

    • Auxiliary Machine Room (AMR)


    Results17

    Results

    • AMR

      • 632,252 polygons

      • 3 CPUs

      • 25,000 occluder polygons

      • No-background z-buffer

      • Approximate culling (0.85 for level 64x64)

      • LOD

      • Lighting; display lists; no triangle strips


    Results18

    Results


    Results19

    Results


    Results20

    Results


    Results21

    Results

    • The power plant model


    Results22

    Results

    • The power plant model

      • 15 million triangles

      • 3 CPUs

      • Visibility pre-processing on a 20x20 grid (~15min)

      • No-background z-buffer

      • 18,000 occluder polygons

      • opacity thresholds from 0.85 and up

      • LOD


    Results23

    Results


    Conclusion1

    Conclusion

    • Goals achieved

      • Generality

        • Any model, any occluder

        • Occluder fusion

      • Speed-up

        • Accelerate interactive graphics

      • Ease of implementation

        • Configurability

        • Robustness


    Conclusion2

    Conclusion

    • Main contributions:

      • Problem decomposition

        • Overlap tests and depth tests

      • Occlusion representations

        • Occlusion maps

        • Depth Estimation Buffer

        • No-Background Z-Buffer


    Conclusion3

    Conclusion

    • Main contributions

      • Hierarchical occlusion maps

        • Analysis of occlusion at multiple resolutions

        • High-level opacity estimation

        • Aggressive approximate culling

        • Levels of visibility

      • The first occlusion culling algorithm for general models and interactive 3-D graphics


    Future work

    Future Work

    • Other implementations...

      • PC’s and games

        • How much can be done in software?

      • Integration into hardware

        • More progressive updates to occlusion representation

        • Less conservative culling

      • Wide-spread use of occlusion culling


    Early splat elimination

    Early Splat Elimination

    • Need: splat visibility test

      • a voxel is only visible if the volume material in front is not opaque

    screen

    occluded voxel: does not pass visibility test

    wall of occluding voxels

    occlusion map = opacity image


    Visibility test naive

    Visibility Test - Naive

    • Check opacity of every pixel within footprint

      • number of pixels to be checked is large

    voxel footprint

    opaque area

    voxel kernel

    opacity buffer


    Visibility test efficient

    Visibility Test - Efficient

    IEEE Trans. Vis. and Comp. Graph. ‘99

    • Compute occlusion map after each sheet-buffer compositing

    project

    do not project

    opacity  threshold

    opacity < threshold

    occlusion map

    opacity = 0


    Early splat elimination results

    Early Splat Elimination - Results


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