VSE Seminar: Decomposition,
Approximation and Reconstruction of Large Geometric Data
Jyh-Ming Lien
Assistant Professor, Department of Computer Science, GMU
Wednesday, Oct 31
10:30 - 11:30 AM
Research Hall, Room 163
Abstract:
Complex geometric data comprising millions to billions of
elements are omnipresent in variety of domains. Examples include
point clouds of urban landscapes, polygon soup representing
buildings in major cities, and combinatorial data structures
induced from the motion of a robot. The unstructured nature of the
data and significant amount of noise pose grand challenges in
designing efficient and practical geometric algorithms.
In this talk, I will provide an overview of these challenges
and algorithmic solutions developed by me and my students
for representing, and manipulating massive geometric data of shape
and motion. In particular, I will focus the discussion on two of
our main contributions: (1) approximate representations via
decomposition and (2) robust mesh reconstruction and repair. I
will also discuss their applications in the areas of CAD, GIS, and
robotics. Our research results have attracted a wide range of
interests from academia, open-source software communities and
industry.
Bio:
Jyh-Ming Lien is an Assistant Professor in the Department of
Computer Science. He directs the Motion and Shape Computing (MASC)
group and is affiliated with the Autonomous Robotics Laboratory at
George Mason University. He received his Ph.D. in Computer Science
from Texas A&M University in 2006. Prior to joining George Mason
in 2007, he was a postdoctoral researcher at UC Berkeley. His recent
work focuses on shape decomposition, approximation and
reconstruction of complex and dynamic 3D geometries. His research
has been supported by NSF, USGS, DOT, AFOSR, and Virginia Center for
Innovative Technology. More images, videos, papers, and software
about his work can be found at: https://masc.cs.gmu.edu/