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

Shape-based depth image to 3D model matching and classification with inter-view similarity

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)
Wohlkinger, W. ; Vision4Robot. Group, Vienna Univ. of Technol., Vienna, Austria ; Vincze, M.

Object recognition and especially object class recognition is and will be a key capability in home robotics when robots have to tackle manipulation tasks and grasp new objects or just have to search for objects. The goal is to have a robot classify 'never before seen objects' at first occurrence in a single view in a fast and robust manner. The classification task can be seen as a matching problem, finding the most appropriate 3D model and view with respect to a given depth image. We introduce a single-view shape model based classification approach using RGB-D sensors and a novel matching procedure for depth image to 3D model matching leading inherently to object classification. Utilizing the inter-view similarity of the 3D models for enhanced matching, the average precision of our descriptors is increased of up to 15% resulting in high classification accuracy. The presented adaptation of 3D shape descriptors to 2.5D data enables us to calculate the features in real time, directly from the 3D points of the sensor, without any calculation of normals or generating a mesh from it which is typical of state-of-art methods. Furthermore, we introduce a semi-automatic, user-centric approach to utilize the Internet for acquiring the required training data in the form of 3D models which significantly reduces the time for teaching new categories.

Published in:

Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on

Date of Conference:

25-30 Sept. 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.