CHC ++:Coherent Hierarchical Culling

Revisited

Oliver Mattausch, Jiří Bittner,Michael Wimmer

Institute of Computer Graphics and Algorithms

Vienna University of Technology

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CHC++: Fast occlusion culling algorithm

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Occlusion Culling

Render only visible geometry → Output sensitivityPreprocessing vs.Online occlusion culling

Preprocessing: Visibility from a region

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Online Occlusion Culling

Query visibility from view point+ No preprocessing+ Dynamic Scenes

Hardware occlusion queries → # visible pixels

Query bounding box Render geometry

Visible?

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Naive method: Hierarchical Stop & Wait

For each node: Issue queryVisible → traverse subtreeInvisible → cull subtree

Problem: Query latencyCPU stalls GPU starvation

Cull Render

Hierarchical Culling

Render

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Previous Work

Coherent Hierarchical Culling (CHC) [Bittner04]Near Optimal Hierarchical Culling (NOHC) [Guthe06]

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CHC

While waiting for query result → traverse / renderKeep query queue

Use coherence, assume node stays (in)visibleFor previously visible nodes

Don‘t wait for query result

Issue query

Render geometry

Use result in next frameResult

available?

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Problems of CHC Too many queries Not GPU friendly

Many state changesBounding box query (8 vertices per draw call)

Can be slower (!) than view frustum culling (VFC)

Most houses visible → Bad view point for CHC

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Properties of NOHC+ Query only if cheaper than rendering+ Mostly better than view frustum culling+ Close to self-defined optimum - Hardware calibration step- Complex set of rules Possible to beat the defined optimum

Can reduce cost of queries Can further reduce # queries

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Improved algorithm: CHC ++

Reduction ofState changesQueriesWait timeRendered geometry

Keeps simplicity of CHC

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Building Blocks of CHC ++

Query batchingReduction of state changesReduction of CPU stalls

MultiqueriesReduction of queries

Randomization Better distribution of queries

Tight bounding volumesReduction of queriesReduction of rendered geometry

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Switch between render / query mode → Need state change (depth write on / off)CHC induces one state change per queryBig overhead on modern GPUs!

Query Batching: State Changes

Idea: Store query candidates in separate queueCollect n nodesSwitch to query modeQuery all nodes

State change

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Query Batching: Previously invisible nodes

Query

Query

Query

Query

Query

Candidate queue Query queue

State change Render

mode

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Previously visible nodesNo dependencies (geometry rendered anyway)Can issue query at any timeHandle them in separate queue

Issue queries to fill up wait timeVery likely no new state change

Issue rest of queries in next frame

Query Batching: Previously visible nodes

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Query Batching: Visualization

CHC: ~100 state changes CHC++: 2 state changes(Max. batch size: 50)

Each color represents a state change

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Node invisible for long timeLikely to stay invisible (e.g., car engine block)Cover many nodes with single query

Test q invisible nodes by single multiqueryInvisible → saved (q – 1) queriesVisible → must test individually, wasted one query

Multiqueries: Idea

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Use history of nodesEstimate probability that node will still be invisible in frame n if it was invisible in frame n - 1Measurements behave like certain exp() function → sufficient in practice

Multiqueries : Minimize #queries

Fitted andmeasured functions

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While node batch not emptyAdd node to multiquery

Use cost / benefit modelQuery size optimal → issue multiquery

Multiqueries: Greedy Algorithm

Visualization: Each color represents a multiquery

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Queues in CHC++

traversal queue

v-queue(visible nodes)

i-queue (invisible nodes)

query queueMultiquery

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Test previously visible nodes each frame → queries wastedAssume visible for t frames

Frame rate drops every t frames

Randomization: Assumed Visibility

Q Q QQ Q Q

Q Q QQ Q Q

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When node becomes visibleRandomize first invocation between [1.. t]Afterwards, test every t frames

Randomization: Idea

Q Q QQ Q Q

Q Q QQ Q Q

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+ Even distribution over frames+ Nodes tested in regular intervals+ Very stable for t between 5 - 10

Tried sophisticated models But they could not beat the simple randomization!

Randomization: Properties

Optimization for bounding volume hierarchy (BVH)For each node → query bounding boxes of children(using single query)Child boxes invisible → Cull node, saved 2 queries Box visible → Traverse node

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Tight Bounding Volumes: Idea

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Subdivide deeper than actual hierarchy depth → tight bounds also for leavesBetter adjusts to shape of objects

Tight Bounding Volumes for Leaves

Tight bounds shown in red

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+ Bounds for interiors reduce queries+ Bounds for leaves reduce geometry

(without overhead of deeper hierarchy)+ More boxes per query not relevant for hardware

Tight Bounding Volumes: Properties

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Results

Powerplant (12M triangles)

Pompeii (6M triangles)

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Results: Powerplant (12M triangles)

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Results: Powerplant (12M triangles)

CHC + Batching + Randomization + Tight Bounds + Multiqueries

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Results: Pompeii (6M triangles)

+ Hierarchical culling algorithm based on CHC+ Kept simplicity of CHC+ Improved several issues of CHC+ Always better than view frustum culling+ Up to 2 – 3 times speedup+ Better than optimum as defined by NOHC Drawback: several parameters

Conclusions

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Multiqueries: Vienna (1M triangles)

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Questions?

THANK YOU FOR YOUR ATTENTION!

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Rendering in modern enginesCollect visible objects in render queueSort by materialsRender everything at once

Rendering single nodes is inefficient (CHC)With batching:

Traverse render queueonly before switch to query mode

Batching: Render engine integration

Future Work

(Semi-)automatically find optimal values for parametersCalibrate parameters during representative walkthroughRemove overhead also for difficult view points

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