1 SIC / CoC / Georgia Tech MAGIC Lab jarekJarek Rossignac Scientific Visualization CS4550 : jarek/lectures

1SIC / CoC / Georgia TechMAGIC MAGIC LabLabMAGIC MAGIC LabLab

http://www.gvu.gatech.edu/~jarekJarek Rossignac

Scientific Visualization

CS4550: http://www.gvu.gatech.edu/~jarek/lectures/ Jarek Rossignac GVC areas What is Scientific Visualization Course objectives Syllabus Text book Grading

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Blends

Jarek (“Y-ah-r-eh-ck”) Rossignac (Rossignol + cognac)http://www.gvu.gatech.edu/~jarek

Maitrise M.E. & Diplome d’Engenieur ENSEM (Nancy, France) PhD E.E. in Solid Modeling (U. of Rochester, NY) IBM TJ Watson Research Center (11 years)

– Senior manger: Visualization, Modeling, Graphics, VR– Visualization: Managed IBM Data Explorer (DX) product R&D– Simplification: 3D Interaction Acceleration (3DIX), OpenGL Accelerator – Geometry compression: VRLM, MPEG-4, awards (ACM TOG)

Georgia Institute of Technology (since 1996)– Professor, College of Computing, School of Interactive Computing– Director of GVU Center, 1996-2001– Compression: Edgebreaker, Awards (IEEE TVCG)– Collaborations: Sweeps (Korea), IsoSurfaces (Spain), Shape Features

(Italy), Surgery planning (Emory)

3D morphs

T=T+T+T

Simplification

CompressionSweeps

SilhouettesInterference



Geometric and Visual Computing areas

Computer Aided Geometric Design (CAGD): Curves/surfaces Solid Modeling: Representations and Algorithms for solids Computational Geometry: Provably efficient algorithms Computer-Aided Design (CAD): Automation of Shape Design Reverse Engineering: Fitting surfaces to scanned 3D points Computer-Aided Manufacturing (CAM): NC Machining Finite Element Meshing (FEM): Construction and simulation Animation: Capture, Design, Simulation of shape behavior Visualization: Graphical interpretations of (large) nD datasets Rendering: Making (realistic) pictures of 3D geometric shapes Image-Based Rendering (IBR): Mix images and geometry Computer Vision: Reconstruction of 3D models from images Virtual Reality (VR): Immersion in interactive environments Augmented Reality (AR): Track and mark-up what you see



What is Scientific Visualization

Modeling: Represent shapes in a computer

Rendering: Make (realistic) pictures of shapes combined with textures, reflectance properties, lighting conditions…

Visualization: Display information so as to reveal relevant meaning/correlation

Scientific: Data is situated (in space and/or time) and often represents scalar or vector fields (measured or computed)

Scientific Visualization: Analyze these scientific data fields, map their properties into shapes and photometric attributes, and render them in a way that reveals important characteristics

Pushes boundaries of modeling and rendering (GPU)




Course objectives

Master tools (math, alg, hardware) for Sci Vis Expand algorithmic problem-solving abilities Practice communication and teamwork skills Learn how to find/read publications in the field Develop a taste for research Have some fun




Syllabus

Intro: Processing, plotting, exaggeration. Project 1: compare plots

Perception: Acuity, Color, Contrast, Optical illusions, Curvature, Motion

Terrains: Terrain rendering and editing. Project 2: compare terrains

Depiction: Surface, Color-coding, Iscocurves, Isoclines, Gradient, Silhouettes

Comparison: Discrepancy, Average, Exaggeration, Animation, Registration

Filtering: Noise reduction, Smoothing, Exaggeration, Local shape analysis

Segmentation:Thresholding, Histograms, Snakes, Level-sets, Pearling

Volvis: Volume visualization, Translucency, Hardware assist

Exploration: View control, Fly-through, Cross-sections, Cut-out, Peeling

Isosurfaces: Local Extraction, Consistency, Tracing, Crust

Flow: Vector fields, Flow visualization

Animation: IsoSurfaces, Flow, Vibrations

Tetrahedra: Tetrahedra, Representation, Simplification, Compression

Multiresolution: Subsample, Simplify, Refine

Transmission: Quantise, Predict, Entropy codecs, Streaming, Visibility order

nD: Time-varying volumes, Higher-dimensional fields, Parallel coordinates,




Text books

Main: Schroeder, Martin, and Lorensen, The Visualization Toolkit - An Object-

Oriented Approach To 3D Graphics, 4th edition, 520 pages, ISBN 1-930934-

07-6, Kitware, Inc. publishers. Engel et al., Real-time Volume Graphics, Course Notes 28, ACM SIGGRAPH

2004

Additional: Hansen and Johnson, The Visualization Handbook, ISBN: 0-12-387582-x,

984 pages, Elsevier, 2004. Nielson, Mueler, and Hagen, Scientific Visualization: Overviews,

Methodologies, and Techniques, 577 pages, IEEE Press, 1997.




Grading

40% Projects (code and reports) + extra credit opportunities– 50% for implementation

• Documented source code • Running implementation that meets the specs• Elegance (conciseness) of the implementation• Extra points: Additions, Extensions, Improvements

– 50% for presentation• Web page with detailed (yet concise) explanations, • Answers to theoretical and algorithmic questions, • Clarity of text, figures, videos• Choice of test cases illustrating the functionality• References and links to material used for inspiration• Extra points: Research questions and ideas, Links to useful sites

15% Quizzes (in class, closed books) 15% Midterm (in class, closed books, 1 page cheat-sheet) 30% Final (in class, closed books, 1 page cheat-sheet)

– Covers whole course, readings, and projects


Documents

1 SIC / CoC / Georgia Tech MAGIC Lab jarekJarek Rossignac Scientific Visualization CS4550 : jarek/lectures