GPU-based Hierarchical Computations for View Independent Visibility Rhushabh Goradia, Prekshu...

GPU-based Hierarchical Computations for View Independent VisibilityRhushabh Goradia, Prekshu Ajmera, Sharat Chandran Sixth Indian Conference on Computer Vision,

Graphics & Image Processing, 2008http://www.cse.iitb.ac.in/~{rhushabh, prekshu, sharat}

Problem Statement

ICVGIP, 2008 Rhushabh Goradia, Prekshu Ajmera, Sharat Chandran

Parallel Fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

Problem Statement

Parallel Fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

Problem Statement

Parallel, fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

Problem Statement

Result Video

VIDEO !

Challenges

O(N3) Time ComplexityN2 point-pairsN occluders considered for every pair

No surface Information

Polygonal Model Point Model

A A BB

[GKSD 07], Visibility Map for Global Illumination in Point Clouds, GRAPHITE 2007

Building the Octree Hierarchy

Visibility between Point Clusters

Discussion: Visibility between Point Clusters O(M2 log M) Time Complexity (for Octree with M leaves)

M2 log M << N3

M << N

… but still not fast enough !

Discussion: Visibility between Point Clusters O(M2 log M) Time Complexity (for Octree with M leaves)

M2 log M << N3

M << N

Contributions O(M2 log M) Time Complexity (for Octree with M leaves)

M2 log M << N3

M << N

Parallel, Fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

Problem Statement & Contributions:

Achieved upto 19x speed-up

O(M2 log M) Time Complexity (for Octree with M leaves)

M2 log M << N3

M << N

Visibility Problem is highly parallel

Contributions

M2 log M << N3

M << N

No compromise on quality Visibility problem is highly parallel

Contributions

M2 log M << N3

M << N

Achieved upto 19x speed-up Visibility problem is highly parallel

No Dynamic Memory Allocation and Recursion No compromise on quality

Contributions

M2 log M << N3

M << N

Achieved upto 19x speed-up Visibility problem is highly parallel No compromise on quality No Dynamic Memory Allocation and Recursion

Contributions

Application to Global Illumination

Point modeled Cornell Room and the Stanford Bunny

Can be extended to the Digital Heritage Projecthttp://research.microsoft.com

Hierarchical Visibility Map (V-map) Construction on CPU

Parallel V-map Construction on GPU

Strategy 1 – Multiple threads per node Strategy 2 – One thread per node Strategy 3 – Multiple threads per node-pair

Results

Conclusion

Results

Conclusion

What is a V-map ?

V-map for a tree is a collection of visibility links for every node in the tree

What is a V-map ?

The visibility link for any node N is a set L of nodes

Node N List L

What is a V-map ?

Every point in any node L is guaranteed to be visible from every point in N V1

Node N V1Visibility Link

List L

What is a V-map ?

Every point in any node L is guaranteed to be visible from every point in N

Node N V1 V2Visibility Link

List L

What is a V-map ?

Every point in any node L is guaranteed to be visible from every point in N

Node N V1 V2Visibility Link

What is a V-map ?

Level 0

(Root)

What is a V-map ?

With respect to at any level,

-- Completely Visible

-- Completely Invisible

-- Partially Visible

Level 0

Level 1

(Root)

What is a V-map ?

Level 0

Level 1

Level 2

(Root)

What is a V-map ?

Level 0

Level 1

Level 2

Level 3

(Root)

(Leaf)

CPU based V-map Construction

Compute Visibility

Level L-2

Level L-1

Level L

Level L-3

Level 0

Level L-2

Level L-1

Level L

Level L-3

Level 0

Look up Look up

All leaf-pairs are visible

Level L-2

Level L-1

Level L

Level L-3

Level 0Complete Visibility Root

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Complete Visibility

New VisibilityLink

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Root

Only some leaf-pairs are visible

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Partial Visibility Root

New VisibilityLink

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

New VisibilityLink

Look up Look up

Compute Visibility

Level L-2

Level L-1

Level L

Level L-3

Level 0

Level L-2

Level L-1

Level L

Level L-3

Level 0

Uses Dynamic Memory Allocation

Compute Visibility

Level L-2

Level L-1

Level L

Level L-3

Level 0

Uses Recursion on the tree

Compute Visibility

Results

Conclusion

Results

Conclusion

Strategy 1: Multiple Threads per Node

I0 I1 I2 I3

Interaction List

Computation by a Single Thread

Compute Visibility

Level L-2

Level L-1

Level L

Level L-3

Level 0

Strategy 1: Multiple Threads per Node

I0 I1 I2 I3

Interaction List

Degree of parallelism limited by the size of the node’s interaction list Serious Limitation: No support for Recursion and Dynamic memory allocation

Discussion:

Results

Conclusion

Strategy 2: One Thread per Node

Degree of parallelism limited by number of nodes per level

Serious Limitation: No support Recursion and Dynamic memory allocation

Discussion:

A I0 I1 I2 I3

N I0 I1

Thread T0

Thread T1

LevelL

Interaction List

Performance increases as we go down the octree

Results

Conclusion

Strategy 3: Multiple Threads per Node-Pair

Level L-2

Level L-1

Level L

Level L-3

Level 0

Compute Visibility between leaves in parallel

Level L-2

Level L-1

Level L

Level L-3

Level 0

Store the results in a boolean array

Level L-2

Level L-1

Level L

Level L-3

Level 0

Look – up from the boolean array

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Root

All leaf-pairs are visible

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Complete Visibility Root

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Complete Visibility

New VisibilityLink

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0Root

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

New VisibilityLink

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

New VisibilityLink

Look up Look up

Level L-2

Level L-1

Level L

Level L-3

Level 0

Look up Look up

CPU v/s GPU

Compute Visibility in parallel

Compute Visibility

CPU Algorithm GPU Algorithm

CPU v/s GPU

Compute Visibility

CPU Algorithm GPU Algorithm

Look – up from the boolean array

Degree of parallelism is high as hundreds/thousands of threads run concurrently most of the time Recursion and Dynamic memory allocation happens on the CPU

Performance is best at octree levels near to root

Achieves upto 19x speed-up

Discussion:

No change in Quality

Results

Conclusion

Results: Visibility Validation

Results: Timings

Bunny in the Cornell Room (1,35,000 points)

Max Speedup : 19X

Results: Timings

Buddha in the Cornell Room (2,30,000 points)

Max Speedup : 14X

Results: Timings

Ganesha & Satyawati in the Cornell Room (3,50,000 points)

Max Speedup : 17X

Results: Application to Global Illumination

Results: Quality Comparison

CPU GPU

Accuracy upto 5 decimal points

Results: Quality Comparison

CPU GPU

Accuracy upto 5 decimal points

Discussion: Implementation Details

Optimal Thread and Block Size Each block (16 x 16) contained 256 threads Every thread-block Grid contained no less than 64 blocks

Optimal Octree Heights Every thread works on a leaf-pair; Multiple threads are independent No Shared Memory Synchronization required With 16 Multi-processors, 64 thread-blocks and 256 threads per block,

we need 16384 leaf-pairs

Asynchronous Computations

Loop Unrolling

Results

Conclusion

Final Note

Parallel implementation of the view-independent mutual point-pair visibility problem on the modern day GPU (G80/G92 architecture) was presented

Several strategies to achieve the desired parallelism were introduced and the most suitable one chosen

Speed-ups upto 19X were reported while maintaining the quality of results

Blocks of 256 threads achieved optimal GPU performance

Good octree heights essential for high speed-ups and accurate visibility results

By viewing this V-map construction as a “pre-processing” step, photo-realistic global illumination rendering of complex point models have been shown

Parallel V-map construction solution can be thought as an intuition to solve other problems involving computing relationships amongst data at various levels in a hierarchy

Thank You

Fractal: Mandel Zoom - Satellite Antenna, Mandelbrot set

Problem Statement

Fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

Problem Statement

Fast computation of view-independent visibility in a complex scene represented as a point model, using a GPU

GPU-based Hierarchical Computations for View Independent Visibility Rhushabh Goradia, Prekshu...

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