Understanding 3D Shapes Jointly

Speaker:        Professor Leonidas J. Guibas
                Stanford University

Title:          "Understanding 3D Shapes Jointly"

Date:           Wednesday, 7 March 2012

Time:           2:00pm - 3:00pm

Venue:          Room 1511 (near lifts 27/28), HKUST

Abstract:

The use of 3D models in our economy and life is becoming more prevalent,
in applications ranging from design and custom manufacturing, to
prosthetics and rehabilitation, to games and entertainment. Although the
large-scale creation of 3D content remains a challenging problem, there
has been much recent progress in design software tools, like Google
SketchUp for buildings or Spore for creatures, or in low cost 3D
acquisition hardware, like the Microsoft Kinect scanner. As a result,
large commercial 3D shape libraries, such as the Google 3D Warehouse,
already contain millions of models. These libraries, however, can be
unwieldy, when the need arises to efficiently incorporate models into
various workflows. Mathematical formulations, efficient algorithms, and
software tools are required to support navigation and search over 3D model
repositories.

In this talk we examine the problem of facilitating these navigation and
search tasks by automatically extracting relationships between shapes in a
collection and understanding their common or shared structure. By
effectively organizing the collection into (possibly overlapping) groups
of related shapes, by separating what is common from what is variable
within each group and across groups, and by understanding the main axes of
variability, we can facilitate a whole slew of operations that make large
3D repositories much more navigable, searchable, compressible, and
visualizable. We will present a quick summary of tools for efficiently
computing informative shape descriptors as well as structure preserving
maps between shapes at different levels of resolution. The main part of
the talk, however, is aimed beyond pairwise relationships, to the study
and analysis of many shapes jointly, looking at networks of maps between
shapes in order to extract joint structure, derive consistent
segmentations, infer phenotypic relationships, etc. This is preliminary
work on what we believe to be a large open area for research -- the joint
understanding of collections of related geometric data sets.

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Biography:

Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of
Donald Knuth. His main subsequent employers were Xerox PARC, Stanford,
MIT, and DEC/SRC. He has been at Stanford since 1984 and is currently the
Paul Pigott Professor of Computer Science (and by courtesy, Electrical
Engineering). He heads the Geometric Computation group and is part of the
Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the
Institute for Computational and Mathematical Engineering. Professor
Guibas' interests span computational geometry, geometric modeling,
computer graphics, computer vision, robotics, ad hoc communication and
sensor networks, and discrete algorithms --- all areas in which he has
published and lectured extensively. Some well-known past accomplishments
include the analysis of double hashing, red-black trees, the quad-edge
data structure, Voronoi-Delaunay algorithms, the Earth Mover's distance,
Kinetic Data Structures (KDS),  Metropolis light transport, and the
Heat-Kernel Signature. Professor Guibas is an ACM Fellow, an IEEE fellow
and winner of the ACM Allen Newell award.