IMI Interdisciplinary Mathematics InstituteCollege of Arts and Sciences

Structure Discovery in 3D Point Cloud Data

  • Sept. 20, 2008
  • 3:30 p.m.
  • LeConte 412

Abstract

Digital models of physical shapes are becoming ubiquitous in our economy and life. Such models are sometimes designed ab initio using CAD tools, but more and more often they are based on existing real objects whose shape is acquired using various 3D scanning technologies. In most instances, the original scanner data is just a set of points sampled from the surface of the object. We are interested in tools for understanding the local and global structure of such scanned geometry for a variety of tasks, including model completion, reverse engineering, shape comparison and retrieval, shape editing, inclusion in virtual worlds and simulations, etc. This talk will present a number of point-based techniques for discovering global structure in such data sets, such as topology extraction, approximate reconstruction with guarantees, or symmetry/repeated pattern detection. The irregular and dynamic sampling in the point data creates new challenges and leads to methods with a distinctly more combinatorial and topological character. Beyond 3D, these same problems remain of interest in higher dimensions, as there is an increasing need to understand massive data sets based on samples obtained from sensors or physical simulations.

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