Computer Modelling Of Fallen Snow Paul Fearing University of British Columbia Vancouver, Canada.

Slides:



Advertisements
Similar presentations
 Over-all: Very good idea to use more than one source. Good motivation (use of graphics). Good use of simplified, loosely defined -- but intuitive --
Advertisements

Surface Simplification Using Quadric Error Metrics Speaker: Fengwei Zhang September
Yang Yang, Miao Jin, Hongyi Wu Presenter: Buri Ban The Center for Advanced Computer Studies (CACS) University of Louisiana at Lafayette 3D Surface Localization.
Efficient access to TIN Regular square grid TIN Efficient access to TIN Let q := (x, y) be a point. We want to estimate an elevation at a point q: 1. should.
Ray tracing. New Concepts The recursive ray tracing algorithm Generating eye rays Non Real-time rendering.
CSE 681 Bounding Volumes. CSE 681 Bounding Volumes Use simple volume enclose object(s) tradeoff for rays where there is extra intersection test for object.
Week 11 - Wednesday.  Image based effects  Skyboxes  Lightfields  Sprites  Billboards  Particle systems.
Ruslana Mys Delaunay Triangulation Delaunay Triangulation (DT)  Introduction  Delaunay-Voronoi based method  Algorithms to compute the convex hull 
By Groysman Maxim. Let S be a set of sites in the plane. Each point in the plane is influenced by each point of S. We would like to decompose the plane.
3/5/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015 Delaunay Triangulations II Carola Wenk Based on: Computational.
Discrete Geometry Tutorial 2 1
Dual Marching Cubes: An Overview
CS447/ Realistic Rendering -- Solids Modeling -- Introduction to 2D and 3D Computer Graphics.
A Bezier Based Approach to Unstructured Moving Meshes ALADDIN and Sangria Gary Miller David Cardoze Todd Phillips Noel Walkington Mark Olah Miklos Bergou.
CPSC 335 Geometric Data Structures in Computer Modeling and GIS Dr. Marina L. Gavrilova Assistant Professor Dept of Comp. Science, University of Calgary,
Modeling Falling and Accumulating Snow (by Moeslund, Madsen, Aagaard and Lerche) K. H. Ko Department of Mechatronics Gwangju Institute of Science and Technology.
Mesh Simplification Global and Local Methods:
“Computer Modelling of Fallen Snow” by Paul Fearing Presented by Luv Kohli COMP238 October 29, 2002.
Lecture 10 : Delaunay Triangulation Computational Geometry Prof. Dr. Th. Ottmann 1 Overview Motivation. Triangulation of Planar Point Sets. Definition.
Filling Arbitrary Holes in Finite Element Models 17 th International Meshing Roundtable 2008 Schilling, Bidmon, Sommer, and Ertl.
Shape Modeling International 2007 – University of Utah, School of Computing Robust Smooth Feature Extraction from Point Clouds Joel Daniels ¹ Linh Ha ¹.
5/1/2000Deepak Bandyopadhyay / UNC Chapel Hill 1 Computer Model‘l’ing of Fallen Snow Paul Fearing University of British Columbia.
Image Morphing : Rendering and Image Processing Alexei Efros.
Randomized Planning for Short Inspection Paths Tim Danner Lydia E. Kavraki Department of Computer Science Rice University.
UNC Chapel Hill M. C. Lin Overview of Last Lecture About Final Course Project –presentation, demo, write-up More geometric data structures –Binary Space.
Computing the Delaunay Triangulation By Nacha Chavez Math 870 Computational Geometry; Ch.9; de Berg, van Kreveld, Overmars, Schwarzkopf By Nacha Chavez.
STREAMER DYNAMICS IN A MEDIA CONTAINING DUST PARTICLES* Natalia Yu. Babaeva and Mark J. Kushner Iowa State University Department of Electrical and Computer.
CSE 681 Ray Tracing Implicit Surfaces. CSE 681 Overview Similar to CSG –Combine primitive objects to form complex object Primitives are “density fields”
Modeling and representation 1 – comparative review and polygon mesh models 2.1 Introduction 2.2 Polygonal representation of three-dimensional objects 2.3.
Junjun Pan 1, Xiaosong Yang 1, Xin Xie 1, Philip Willis 2, Jian J Zhang 1
Multi-Layered Navigation Meshes Wouter G. van Toll, Atlas F. Cook IV, Roland Geraerts ICT.OPEN 2011.
11/30/04© University of Wisconsin, CS559 Fall 2004 Last Time More modeling: –Hierarchical modeling –Instancing and Parametric Instancing –Constructive.
10/21/03CS679 - Fall Copyright Univ. of Wisconsin Last Time Terrain Dynamic LOD.
Dispersion due to meandering Dean Vickers, Larry Mahrt COAS, Oregon State University Danijel Belušić AMGI, Department of Geophysics, University of Zagreb.
A Navigation Mesh for Dynamic Environments Wouter G. van Toll, Atlas F. Cook IV, Roland Geraerts CASA 2012.
1 Three dimensional mosaics with variable- sized tiles Visual Comput 2008 報告者 : 丁琨桓.
Basics of Rendering Pipeline Based Rendering –Objects in the scene are rendered in a sequence of steps that form the Rendering Pipeline. Ray-Tracing –A.
ADA: 14. Intro to CG1 Objective o give a non-technical overview of Computational geometry, concentrating on its main application areas Algorithm.
Surface Simplification Using Quadric Error Metrics Michael Garland Paul S. Heckbert.
A D V A N C E D C O M P U T E R G R A P H I C S CMSC 635 January 15, 2013 Quadric Error Metrics 1/20 Quadric Error Metrics.
Algorithms for Triangulations of a 3D Point Set Géza Kós Computer and Automation Research Institute Hungarian Academy of Sciences Budapest, Kende u
Geometric Modeling using Polygonal Meshes Lecture 1: Introduction Hamid Laga Office: South.
1 3D virtual mosaics: Opus Palladium and mixed styles Visual Comput 2009 報告者 : 丁琨桓.
Interactive surface reconstruction on triangle meshes with subdivision surfaces Matthias Bein Fraunhofer-Institut für Graphische Datenverarbeitung IGD.
02/18/05© 2005 University of Wisconsin Last Time Radiosity –Converting the LTE into the radiosity equation –Solving with Gauss-Seidel relaxation –Form.
Review of Two-Scale Particle Simulation Paper by: Barbara Solenthaler ETH Zurich Markus Gross ETH Zurich.
Rendering Overview CSE 3541 Matt Boggus. Rendering Algorithmically generating a 2D image from 3D models Raster graphics.
Rendering Implicit Plots. What is an Implicit Plot? An implicit plot is a plot of all the points satisfying equation:  f(x,y) = 0 (for some function.
Spatial Interpolation Chapter 13. Introduction Land surface in Chapter 13 Land surface in Chapter 13 Also a non-existing surface, but visualized as a.
Managing the Level of Detail in 3D Shape Reconstruction and Representation Leila De Floriani, Paola Magillo Department of Computer and Information Sciences.
Vehicle Segmentation and Tracking From a Low-Angle Off-Axis Camera Neeraj K. Kanhere Committee members Dr. Stanley Birchfield Dr. Robert Schalkoff Dr.
Presentation of the paper: An unstructured grid, three- dimensional model based on the shallow water equations Vincenzo Casulli and Roy A. Walters Presentation.
Point Sprites Course Information CVG: Programming 4 My Name: Mark Walsh Website: Recommended.
Representation and modelling 3 – landscape specialisations 4.1 Introduction 4.2 Simple height field landscapes 4.3 Procedural modeling of landscapes- fractals.
Ray Tracing Fall, Introduction Simple idea  Forward Mapping  Natural phenomenon infinite number of rays from light source to object to viewer.
BOĞAZİÇİ UNIVERSITY – COMPUTER ENGINEERING Mehmet Balman Computer Engineering, Boğaziçi University Parallel Tetrahedral Mesh Refinement.
Bounding Volume Hierarchy. The space within the scene is divided into a grid. When a ray travels through a scene, it only passes a few boxes within the.
UNC Chapel Hill M. C. Lin Delaunay Triangulations Reading: Chapter 9 of the Textbook Driving Applications –Height Interpolation –Constrained Triangulation.
1/57 CS148: Introduction to Computer Graphics and Imaging Geometric Modeling CS148 Lecture 6.
APE'07 IV INTERNATIONAL CONFERENCE ON ADVANCES IN PRODUCTION ENGINEERING June 2007 Warsaw, Poland M. Nowakiewicz, J. Porter-Sobieraj Faculty of.
Bigyan Ankur Mukherjee University of Utah. Given a set of Points P sampled from a surface Σ,  Find a Surface Σ * that “approximates” Σ  Σ * is generally.
Polygon Triangulation
Processing Images and Video for An Impressionist Effect Automatic production of “painterly” animations from video clips. Extending existing algorithms.
Autonomous Robots Robot Path Planning (2) © Manfred Huber 2008.
Vehicle Segmentation and Tracking in the Presence of Occlusions
Water Droplet Simulation
Stochastic Microgeometry for Displacement Mapping
© 2003 University of Wisconsin
Mesh Control using Size Functions and Boundary Layers
Snow and Ice Modeling Ted Kim April 24, 2002 COMP 259.
Presentation transcript:

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

Goal

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

Decomposition of Gravity

Global of the Snow Model Snow Location Snow Stability Snow Surface Wind

Snow Location Snow bridge across gaps Cornice and Overhang

Snow Location

Related Work Snows –Metaballs Stochastic Motion Snow Shadows Flow and Change Dust Accumulation

Related Work Three Major Models –Volume-based model –Surface-based model –Hybrid-based model

Volume-based model

Surface-based model

Hybrid-based model

Contribution Accumulation Model Stability Model

snow pipeline Overview of the snow pipeline Commercial software –Alias Wavefront 96 (Shader libraries, Rendering)

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

Entities Launch site Subdivision area (or Launch area)

Entities Edge group Drops

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.

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 )

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.

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.

Launch sites There is no stability in this example

Occlusion Boundary Transition Zone

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

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.

Importance Ordering Limits –Prevent launch sites from increasing very complex occlusion boundaries. Steepness –Launch sites that are too steep to support much snow.

Importance Ordering Camera –Sites closer to the camera receive more particles, greater refinement and accuracy. User –“ Boring ” –“ Interesting ”

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.

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

Launch Site Meshing In practice, many surface are small and isolated (such as pine needle) Significant meshing occurs on large, connected surface (such as the ground)

Edge Groups Edge groups are primarily used for –Avalanche –Denoting sharp boundary –Snow may slide off from one edge group to another

Edge Groups Drops Bordered by XY silhouette edge (in red)

Edge Groups This graph show a model (knot ) that our meshing algorithm considers hard.

Initial Particle Distribution Final mesh Initial launch sites Final mesh Final launch sites

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.

Snowflake Motion Circumference (swirl) Radius (wiggle) Z step resolution

Snowflake Motion Changing a flake ’ s Z incremental test change the flake ’ s direction.

Snowflake Motion At each step –The value of is randomly chosen from a normal distribution. –“ Area of effect ” increases from 1 cm to 4 cm to 7 cm from left to right. = 1 cm

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

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

Locating Particles in the Sky

Writing in the Sky

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

Stability Test 1. Compute AOR between s and all neighbors n i lower than s. 2. For each i with an AOR to steep to support snow, perform an obstacle test between s and n i. 3. Evenly shift snow from s to all neighbors n i. 4. Repeat steps 1 to 3 until no unstable neighbors left, or s is bare of snow.

Moving Snow over Edges

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

Implicit Function

Validation Validation of snow-covered scenes is hard. –Uncontrollable –Unknown environmental factors

Future Work Physically realistic Animation Time –Large model

Result