Sample Based Geometric Modeling for Graphics and Visualization

Instructor Tamal K. Dey

Course Description

Recent advances in scanning technology and scientific
simulations can generate sample points from a geometric domain
at ease. Inferring the shape and their features from such
a simple light-weight input can be a very effective modeling paradigm
across many areas of science and engineering. In this talk
we will go over the techniques developed for such modeling
paradigms. In particular, we cover algorithms for
(i) extracting a surface out of point samples, (ii) computing
an approximate medial axis of the sampled object, (iii) segmenting
the object into so called ``features". Theoretical concepts along with
experimental results will be presented.

Course Materials


Power point presentation for the class

Oversampling I
Oversampling II
Large Data
Medial axis approximation
Shape segmentation and matching
Dimension detection 

Surface reconstruction
Undersampling detection
Large Data
Medial axis
Shape segmentation

Tutorial for the Fall School at Berlin, October, 2003


DL 266 TR 12:30-1:48
Office: DL483
Office Hours :  TR 2:00-2:30 or by appointment


Grading will be based on three things.
A. Experiment with the Cocone software (will be provided) on data from your application
and study its results.
B. A  Term paper at the end of the course on a related topic of your choice.
C. Your class participation.