joe kniss scientific computing and imaging institute university of utah volume effects: modeling...

Joe Kniss

Scientific Computing and Imaging Institute

University of Utah

Volume Effects:Modeling Smoke, Fire, & Clouds

Classification & Optical Properties Joe KnissSiggraph 2004

Volume Modeling

• Acquired data (rarely available)• Simulated (implicit)• Procedural (explicit)


Volume Modeling

• Acquired data (rarely available)• Simulated (implicit)

– Fedkiw (smoke, fire, fluids)– Harris, Dobashi, Nishita (clouds)

- GP-GPU tomorrow

• Procedural (explicit)

Fedkiw et al.Dobashi et al.


Volume Modeling

• Acquired data (rarely available)• Simulated (implicit)

– Fedkiw (smoke, fire, fluids)– Harris, Dobashi, Nishita (clouds)

- GP-GPU tomorrow

• Procedural (explicit)

Wednesday : Course 32, GPGPU, Full day


Volume Modeling


– Constructive volume modeling & animation– Build volume from basic blocks– Blinn, Perlin, Ebert

Perlin Schpok et al.


Volume Modeling


– Constructive volume modeling & animation– Build volume from basic blocks– Blinn, Perlin, Ebert

Wednesday: Course 31, Elements of Nature, Full day


Simulation

• Navier-Stokes equations– (Nearly) Always assume divergence free– Time dependent– Initial conditions + Mathematical constraints

• Other options:– Boltzman lattice– Reaction diffusion & coupled map lattice

• Current conditions “implied” by initial conditions and simulation

• Key concept: Project out error and instability– Hodge-Helmholtz decomposition


Procedural

• Evaluate a function for each fragment– Generate data– Generate optical properties– Modify position– Modify optical properties

• Key components:– Spatial primitives– Noise


Primitives

• Planes – Distance to plane– Half space

• Blobs (ellipsoids)– Gaussian blob h*exp( -(pos – cent)2 /width)

• Cylinders (volume of rotation)– Any curve based on distance to line segment

• Cones – Inside / outside– Area of circle

w


Noise

• Perlin noise (ideal?)– Not a reality on GPUs, yet…

• Need to use 3D texture• “Perturb”

– Position (rolling waves)– Optical properties (inhomogeneous clouds)

• Position– Add vector to current tex-coord

• Optical properties– Multiply or add to opacity etc…


Noise

• Fill 3D texture with random values (3/4 vectors)• Blur values (hide grid)• Set to wrap• Set texture coords

– Multiple copies (multiple texture reads)– Need constants to scale higher frequencies

• In fragment program– Read from noise texture a different frequencies– Scale and sum vectors (mad op)– Add to “data” tex-coord/ position/ optical properties


Divergence Free Noise

• Don’t just use any old random noise field for flow like phenomena (position perturbation)

• Create vector field that is divergence free to begin with

• Curl( random field ) = 0 divergence– Great for turbulence

• Rotation about a cylinder = vortex• Constant direction = prevailing wind• Sum of divergence free fields = divergence free• Keep divergence around for optical properties?


Quick review• Grad, differential linear operator:

– { dx, dy, dz }; grad * f = gradient (f is scalar)

• Divergence: div(V) = Dot( grad, V ) (v is vector)– dx vx + dy vy + dz vz

• Curl: curl(V) = Cross( grad, V )– {(dy vz – dz vy), (dz vx – dx vz), (dx vy – dy vx)}

• Divergence free: div( V ) = 0– div( curl ( V ) ) = 0– div( Const( v ) ) = 0

• Curl = rotation– Orthogonal to original flow

• Dot distributes– Dot( grad, u + v ) = Dot( grad, u ) + Dot( grad, v )


Flow noise – visual example

Original Curl free Divergence free

Tong et al.


Flow noise – Key idea

• For random turbulence for perturbation– Use curl of random noise field

• Avoid cusps in flow• Subtle, but important• Construct “valid” flow field exactly how you want

it.


Flow perturbation – Tips

• Everything is backwards– Gather rather than scatter

• Modify position based on where you came from• NOT where the flow is going (opposite)


Practical Advice• Split relevant computation between vertex and

fragment programs– Every instruction counts!– For blobs; center/distort in VP

• Still need to use to textures!– Even if entirely procedural, texture is fast– Use for volume of rotation, height fields etc…

• Try it multiple ways, and get timings!• Volume rendering == FILL BOUND

– Long programs can be bad– Try pre-integrated transfer functions – Try zero-mean noise (only need modulo)– Compute on tessellated mesh and interpolate for frags


More Practical Advice

• Move harmonics of noise independently– Example movie


Meta-programming

• Procedural generation of procedural vertex/fragment programs

• Simplifies a potentially exponential explosion of GPU programs

• Use SH from Waterloo• Design your own lazy meta interface

– Keep symbol table– Attribute maps– Use C++ for your fragment programs


Height fields and more

• Height field == Huge detail, simple canvas• Leverage p-buffers and auto-mipmap• Compute “height” and compare to map-altitude• Keep it fuzzy• Align height field with view port for optimal

silhouette


Faking it

• 2D Billboard• 2.5 D Height field• Cache “Render to 3D texture”


Example – fluffy clouds

+

+ +

np

c

D = exp( -(c – ( x+n*dot(x-p,n)*1/(1+dot(x-p,n))a ) + s*noise(x) )


Summary

• Volume Construction– Needed for dynamic effects

• Several choices – Simulation– Procedural

• Consider flexibility of vertex/fragment programs– Longer programs, minimize cache misses– Need to procedurally generate these programs

• Flow noise• Height fields & caching• User interface is important

– Primitives and deformers


Acknowledgements

• Nvidia• Ati• Doe HPCS Graduate Fellowship• Laurence Livermore National Lab

• More info:– www.cs.utah.edu/~jmk

joe kniss scientific computing and imaging institute university of utah volume effects: modeling...

Documents

day slide

joe kniss scientific

modeling smoke

spatial primitives noise

noise perlin noise ideal

procedural explicit

d texture

outside area of circle