computer modelling of fallen snow paul fearing university of british columbia vancouver, canada

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Computer Modelling Of Fall en Snow Paul Fearing University of British Columbia Vancouver, Canada

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Page 1: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Computer Modelling Of Fallen Snow

Paul FearingUniversity of British ColumbiaVancouver, Canada

Page 2: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Goal

Page 3: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Goal

Page 4: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Introduction

• Related Work• Snow Accumulation• Snow Stability• Implicit Function• Validation• Future Work• Conclusion

Page 5: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Decomposition of Gravity

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Global of the Snow Model

• Snow Location• Snow Stability• Snow Surface• Wind

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Snow Location

• Snow bridge across gaps• Cornice and Overhang

Page 8: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Snow Location

Page 9: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Related Work

• Snows– Metaballs

• Stochastic Motion• Snow Shadows• Flow and Change• Dust Accumulation

Page 10: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Related Work

• Three Major Models– Volume-based model

– Surface-based model

– Hybrid-based model

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Volume-based model

Page 12: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Surface-based model

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Hybrid-based model

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Contribution

• Accumulation Model

• Stability Model

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snow pipeline• Overview of the snow pipeline

• Commercial software– Alias Wavefront 96 (Shader libraries, Rendering)

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Entities• World

– Sky, Ground, wind, Original input model and allocated snow

• Model– The set of input polygons– Connected and Non-connected component

• Face– Primary structure

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Entities• Launch site• Subdivision area (or Launch area)

Page 18: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Entities

• Edge group• Drops

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Entities• Snow planes

– Top snow planes(Triangular )

– Edge snow planes(Quadrilateral )

• Avalanche

• Avalanche Flake– When an avalanche hits a drop, it is converted

into a number of particles.

Page 20: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Snow Accumulation• Occlusion Boundary

– The “Flake Flutter” effect eventually produces an occlusion boundary between completely blocked and unblocked areas.

• Influence– Amount of snow– Closeness of the occlusion

to the ground– Fluttering effect (wind )

Page 21: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Launch sites

• Shoot particles– This approach allow launch sites on each

surface to emit a series of particles aimed upwards towards a sky bounding plane.

Page 22: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Launch sites

– Whenever a launch site has a sufficiently different sky occlusion from an adjacent neighbor, a new launch site is added at the perturbed midpoint to be refine the transition.

– Likewise, launch sites can be merged whenever all surrounding neighbors have identical sky occlusions.

Page 23: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Launch sitesThere is no stability in this example

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Occlusion Boundary• Transition Zone

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Importance Ordering

• Resolution– How many launch sites the face needs.

– How many particles each site should shoot.

• Determination– Order of site testing

– Improve the resolution

Page 26: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Importance Ordering

• Completeness– Global approximation

• Area– To prevent missing occlusion, large

area may need more particles per launch site and more initial sites.

• Neighborhoods– Add or remove the launch site.

Page 27: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Importance Ordering• Limits

– Prevent launch sites from increasing very complex occlusion boundaries.

• Steepness– Launch sites that are too steep to support

much snow.

Page 28: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Importance Ordering• Camera

– Sites closer to the camera receive more particles, greater refinement and accuracy.

• User– “Boring”– “Interesting”

Page 29: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Launch Site Meshing• Launch site surfaces are

represented as triangles.(the original base models)

• All upwards-facing triangles are initially allocated at least one launch site.

• Additional launch sites are allocated base on the importance ordering of the surface.

Page 30: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Launch Site Meshing

• Launch sites are connected in the Delaunay triangulation, where each launch site is responsible for its own immediately surrounding Voronoi area.

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Launch Site Meshing

• In practice, many surface are small and isolated (such as pine needle)

• Significant meshingoccurs on large, connected surface (such as the ground)

Page 32: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Edge Groups• Edge groups are primarily used for

– Avalanche– Denoting sharp boundary– Snow may slide off from one edge group to

another

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Edge Groups

• Drops• Bordered by

XY silhouette edge (in red)

Page 34: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Edge Groups• This graph show a model (knot ) that

our meshing algorithm considers hard.

Page 35: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Initial Particle Distribution

• Final mesh

• Initial launch sites

• Final mesh

• Final launch sites

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Snowflake Motion

• Have no experimental data– How flakes of various sizes and shapes

move when dropped from a significant height.

• Provide some parameters to simulate snowflake motion.

Page 37: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Snowflake Motion

• Circumference(swirl)

• Radius(wiggle)

• Z step resolution

rf

h

Page 38: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Snowflake Motion• Changing a flake’s Z incremental test change

the flake’s direction.

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Snowflake Motion• At each step

– The value of is randomly chosen from a normal distribution.

– “Area of effect ”

rf

increases from 1 cm to 4 cm to 7 cm from left to right. = 1 cm

F F

Page 40: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Wind

• The “wind influence ” is essentially a velocity vector for every point x, y, z in space.

Page 41: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Intersection Bucketing• Dividing the XY plane into a regular grid of buckets.

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Locating Particles in the Sky

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Writing in the Sky

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Snow Stability• All launch sites are initially stored by Z height

plus accumulation.• Angle of Repose (AOR)

• Fresh snow => 90

o

• Slush snow=> 15

o

Page 45: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Stability Test1. Compute AOR between s and all neighbors ni lower than s.2. For each i with an AOR to steep to support snow, perform an obst

acle test between s and ni . 3. Evenly shift snow from s to all neighbors ni .4. Repeat steps 1 to 3 until no unstable neighbors left, or s is bare of

snow.

Page 46: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Moving Snow over Edges

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Moving Snow over Edges

Page 48: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Implicit Function

• Each snow volume is converted into one of several different implicit function types.– Gap bridging, Edge bulges, Wind cornices

Page 49: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Implicit Function

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Implicit Function

Page 51: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Validation• Validation of snow-covered scenes is hard.

– Uncontrollable– Unknown environmental factors

Page 52: Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada

Future Work

• Physically realistic• Animation• Time

– Large model

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Result

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Result

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Result