Detection of Symmetries and Repeated Patterns in 3D Point Cloud Data

author: Leonidas J. Guibas, Stanford University
published: Dec. 5, 2008,   recorded: November 2008,   views: 1394
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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, but a very large set, of points sampled from the surface of the object. We are interested in tools for understanding the local and global structure of such large-scale 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 3D data sets, including partial and approximate symmetries, shared parts, repeated patterns, etc. It is also of interest to perform such structure discovery across multiple data sets distributed in a network, without actually ever bring them all to the same host.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: