Cart (Loading....) | Create Account
Close category search window
 

Room-structure estimation in Manhattan-like environments from dense 2½D range data using minumum entropy and histograms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Olufs, S. ; Vienna Univ. of Technol., Vienna, Austria ; Vincze, M.

In this paper we propose a novel approach for the robust estimation of room structure using Manhattan world assumption i.e. the frequently observed dominance of three mutually orthogonal vanishing directions in man-made environments. First, separate histograms are generated for every major axis, i.e. X, Y and Z, on stereo data with an arbitrary roll, pitch and yaw rotation. These histograms are maintained in the fashion of quadtrees. Using the traditional Markov particle filters and minimal entropy as metric on the histograms, we are able to estimate the camera orientation with respect to orthogonal structure. Once the orientation is estimated we extract hypothesis of the room structure by exploiting 2D histograms, i.e. X/Y, Z/Y, Z/X, using mean shift clustering techniques. Finally, the hypotheses are evaluated with the real data and false hypothesis are pruned. We also show the robustness of our approach with respect to noise in real world data.

Published in:

Applications of Computer Vision (WACV), 2011 IEEE Workshop on

Date of Conference:

5-7 Jan. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.