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Scientific Visualization

Scientific Visualization. CS4550 : http://www.gvu.gatech.edu/~jarek/lectures/ Jarek Rossignac GVC areas What is Scientific Visualization Course objectives Syllabus Text book Grading. Interference. Silhouettes. T=T+T+T. Sweeps. Compression. 3D morphs. Blends. Simplification.

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Scientific Visualization

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

  2. Interference Silhouettes T=T+T+T Sweeps Compression 3D morphs Blends Simplification 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)

  3. 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

  4. 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)

  5. 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

  6. 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,

  7. 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.

  8. 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

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