CSE 612 - Advanced Visualization


General Info:

Instructor: Prof. Klaus Mueller
    Office hours: Tu 3-5 pm (send email for other arrangements), CS 2428
    Phone: 632-1524
    Email: mueller AT cs DOT sunysb DOT edu

Meeting time and venue:
    CS 2311 (new Auditorium), Tuesdays and Thursdays 5:20-6:40 pm

Summary (from course bulletin):
    This course discusses advanced concepts in the area of volumetric data modeling and visualization. Topics included are: Visual exploration of multi-variate and multi-dimensional datasets on regular and irregular grids, modeling of natural phenomena and simulation of realistic illumination, volumes as magic clay for sculpting and deformation effects, non-photorealistic rendering for illustration and artistic works, information-centric exploration of large datasets, and exploitation of hardware for acceleration. The course strives to provide a snapshot on the current state of the art and will be supported mostly by recent research papers. Students will expand on a topic of their choice by completing an individual project.

    Graduate standing
    CSE 564: Scientific Visualization

    Research papers and handouts

    Project: 70%
    Presentations: 20%
    Quizzes: 10%

    Pick a project, either from a list provided early in the semester, or on your own. It should be a project of substance, in accordance to the level of the class and grade-percentage allocated. 
Course procedures:
    Please attend every session. The occasional pop-quizzes are meant to make sure that you’ve read the paper when one was assigned to read. The quizzes won’t go into much depth, but they will be decisive in what they’re designed for. Your presentation should be related to the topic you’ve chosen for your project. Mid-way through the course, we will also have 2 sessions allocated for progress reports on each project in form of 15-20 minute presentations. A lively discussion among the class is highly encouraged. The project presentations (and the presentations in general) are meant as a mechanism to improve the project, clarify pertaining issues, and come up with cool new ideas, enhancements, and applications. There have been a number of published research papers that resulted from last year’s class projects.
    This year, a special emphasis is laid on implementions that will run, or at least have good prospects to run, on graphics hardware, although this is not a requirement. It is understood that not all algorithms are well suited to hardware acceleration, but nevertheless, recognizing properties that make hardware acceleration possible is a goal of this class.

Course topics (for  actual presentation times see website):

Getting to know the data:
    Application domains - computational science, engineering, design, medical, data warehouses, sensors, statistics, business, entertainment, the arts.
    Data generation - simulation, acquisition, assimilation, modification.
    Data representations - grids, points, multi-valued, multi-modal, time-variant, high-dimensional, vectors, tensors.

Theoretical background:
    Tracking the energy - the high-albedo volume rendering integral, radiative transfer.
    Signal processing theory- Fourier analysis, wavelets, implicit functions.
    Signal processing practice - reconstruction, filtering, sampling, compression.

General issues:
    Perception - modeling the human visual system, metrics to gauge image quality.
    User interfaces - for the general user, developer, researcher, student.
    Quality vs. speed - where to strike the compromise.
    Beyond the O-notation - the importance of cache, MMX, pipelines, GPUs.

Rendering algorithms:
    Regular grids - recap from CSE564 (raycasting, splatting, shear-warp, texture-mapping).
    Irregular grids - curvilinear, unstructured.
    Higher dimensional and multivariate - tensor rendering, time-varying, N-dimensional
    Alternative algorithms - Fourier domain rendering, wavelet domain rendering.

Data Representations:
    Primitives - points, micro-textures, cells, CSG, 3D NURBS.
    Decompositions - octree, space-time, multi-resolution hierarchies.
    On-the-fly computed promitives - images, super-rays, image-based accelerations

Non-photorealistic rendering:
    Techniques - 3D image-processing on-the-fly.
    Applications - Illustrations, Impressionist and Pen-and-Ink rendering, extension to SciVis.

Segmentation and registration:
    Representations - Contour graphs, Reeb graphs.
    Interactive segmentation - transfer function-based, analysis of moments, segmentation by example
    Techniques for finding good transfer functions
    Feature extraction in scientific datasets - vortex cores, complex flows, singularities
    Boundary finding - active contours, balloons.

Modeling of physical processes and phenomena and simulation of realistic illumination and appearance:
    Volumetric radiosity - optical models, photon maps, volumetric backprojection, Monte-Carlo
    Realistic appearance models - BRDFs, reflectance fields, synthesized textures.
    Amorphous phenomena - clouds, smoke, fire, water, gemstones.
    Physically-based simulation of physical processes - melting, ablation, breaking, fracturing.
    Solution mechanisms for physical processes - Navier-Stokes, lattice methods.
    Efficiency considerations -  cumputation management, GPU acceleration.

Volumes as magic clay:
    Deformations - volumetric sculpting, warping, morphing.
    Effects to exterior influences- thawing, melting, aging.
    Level-of-detail modeling of real materials.
    Sensitive rendering - rendering with haptic feedback.

    Consumer graphics boards - programmable GPUs (GeForce FX, ATI Radeon).
    Parallel architectures - distributed vs. shared memory, network of workstations (NOW).
    Specialized hardware - VolumePro, Vizard

Information visualization:
    Information representation - glyphs, graphs, parallel coordinates, tree plots
    Feature extraction - the art of finding and displaying the most relevant data
    Visual data mining and monitoring - explorative visualization with the scientist in the loop
    The inter-relationship between information visualization and scientific visualization

Visualization systems and case studies:
    SciRun - Computational steering package from the University of Utah.
    Collaborative visualization system with network support
    VTK - Visualization Toolkit from Kitware, Inc.
    Data Explorer - open-source system, formerly IBM.
    Surgical simulator with haptic feedback
    TeraGrid - a new large effort to facilitate data sharing and distributed computing over the web.