M. Jędrzejewski, K.Marasek, Warsaw ICCVG, 22-24.09.2004 Multimedia Chair Computation of room acoustics using programable video hardware Marcin Jędrzejewski

M. Jędrzejewski, K.Marasek, Warsaw ICCVG, 22-24.09.2004

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Computation of room acoustics using programable video hardware

Marcin Jędrzejewski Krzysztof Marasek


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Presentation plan

Acoustics computational methods Graphical Processing Unit ( GPU ) – programming model Used acoustic model Used algorithms and data structures Implementation on GPU Results Conclusions and further work Demo movie


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Room acoustics

Geometrical methods- Beam tracing

Not very scaleable

Ray tracing Point sound source Receiver aproximated by sphere Each position change requires

recomputation

- Image sources Computationally ineffective

Echogram:


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Room representation

3D geometry Walls are made of polygons Each wall contains information on its absorption coeficient Scene contains also positions of sound source

and receiver


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GPU – programming model

Streaming processor - the same program is executed parallely but with different data on input. Each program produces output data.

Programs that are executed are also known as kernels or Pixel Shaders

HLSL as programming language, very similar to C++ Input and output data is stored in a form of textures which are

blocks of memory stored on video card


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Program execution

Data (like rays) are loaded to input texture Quad (rectangle composed of two triangles) is rendered, for each

processed texel, pixel shader is executed HLSL program can read from many different textures but can write up to

16 floating point values (using MRT) Many passes of this algorithm


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Acoustics model

One frequency band is used for

material absorption High number of reflections (~15 - 25) No diffraction, refraction, diffusion

– purely specular model


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Space division algorithms used

Constructive Solid Geometry (CSG) : to remove all illegal geometry Binary Space Partitioning (BSP) : to partition space into

convex subspaces Portal calculation – to find ways

between subspaces Further subspaces division

required for efficient GPU

implementation


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Execution flow

1. CSG, BSP, Portals, ...

2. Generation of rays on sphere surface, uploading textures to video card

3. Propagation of rays through portals and reflecting them from walls

4. Retrieving video memory with computed rays and building echogram

5. Using echogram to generate spatial sound

Loading textures: state data and data

structures

Data structures computation

Multipass rendering

Building echogram

Auralization

Real timePrecomputation phase

1 2

3

4

5


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Results

Almost 100 mln ray - triangle intersections checks per second Computation of above 16000 rays with 10 reflections in two

room enviroment takes ~30ms

Model Poly count Precomputation CPU GPU

Two rooms 24 1,4s 0,5s 0,016s

House 192 1,89s 0,37s 0,025s

Office 390 4,54s 0,82s 0,027s

Church 390 1,09s 0,82s 0,033s


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Conclusions and further work

Real-time computation of echogram with the use of ray tracing algorithm

CPU can execute other code parallely when GPU is computing ray reflections

Making use of NVIDIA Geforce 6800 or ATI X800 cards Making use of PCI-Express architecture (even 16ms speed up

possible) Mapping cone or pyramid tracing algorithms for early reflections

and using raytracing for late reverberation Better optimizations with model 3 of PS and VS


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Demo movie

Documents

M. Jędrzejewski, K.Marasek, Warsaw ICCVG, 22-24.09.2004 Multimedia Chair Computation of room acoustics using programable video hardware Marcin Jędrzejewski