Pose Estimation using Local Structure-Specific Shape and Appearance Context

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Abstract

We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how to utilize this for the estimation of the alignment pose between two point sets. We present experiments both in controlled and real-life scenarios to validate our approach.
Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Automation (ICRA)
Number of pages8
PublisherIEEE
Publication date2013
Pages2080-2087
ISBN (Print)978-1-4673-5641-1
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Robotics and Automation: Anthropomatics - Technologies for Humans - Kongresszentrum Karlsruhe, Karlsruhe, Germany
Duration: 6. May 201310. May 2013

Conference

Conference2013 IEEE International Conference on Robotics and Automation
LocationKongresszentrum Karlsruhe
Country/TerritoryGermany
CityKarlsruhe
Period06/05/201310/05/2013

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