Nissler, Christian und Marton, Zoltan Csaba und Suppa, Michael (2013) Sample Consensus Fitting of Bivariate Polynomials for Initializing EM-based Modeling of Smooth 3D Surfaces. In: IEEE International Conference on Intelligent Robots and Systems. International Conference on Intelligent Robots and Systems, 2013-11-03 - 2013-11-08, Tokio, Japan. doi: 10.1109/IROS.2013.6696962.
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Kurzfassung
This paper presents a method for finding the largest, connected, smooth surface in noisy depth images. The formulation of the fitting in a Sample Consensus way allows the use of RANSAC (or any other similar estimator), and makes the method tolerant to low percentage of inliers in the input. Therefore it can be used to simultaneously segment and model the surface of interest. This is important in applications like analyzing physical properties of Carbon-fiber-reinforced polymer (CFRP) structures. Using bivariate polynomials for modeling turns out to be advantageous, allowing to capture the variations along the two directions on the surface. However, fitting them efficiently using RANSAC is not straightforward. We present the necessary preand post-processing, distance and normal direction checks, and degree optimization (lowering the order of the polynomial), and evaluate how these improve results. Finally, to improve the initial estimate provided by RANSAC, an Expectation Maximization approach is employed, converging to the best solution. The method was tested on high-quality data and as well on real-world scenes captured by a RGB-D camera. We will publish the method as part of the Point Cloud Library.
elib-URL des Eintrags: | https://elib.dlr.de/87117/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Sample Consensus Fitting of Bivariate Polynomials for Initializing EM-based Modeling of Smooth 3D Surfaces | ||||||||||||||||
Autoren: |
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Datum: | November 2013 | ||||||||||||||||
Erschienen in: | IEEE International Conference on Intelligent Robots and Systems | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/IROS.2013.6696962 | ||||||||||||||||
Name der Reihe: | Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Surface Estimation, 3D fitting, 3D modeling with polynomials, RANSAC, Expectation Maximization, Segmentation | ||||||||||||||||
Veranstaltungstitel: | International Conference on Intelligent Robots and Systems | ||||||||||||||||
Veranstaltungsort: | Tokio, Japan | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 3 November 2013 | ||||||||||||||||
Veranstaltungsende: | 8 November 2013 | ||||||||||||||||
Veranstalter : | IEEE/RSJ | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Technik für Raumfahrtsysteme | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R SY - Technik für Raumfahrtsysteme | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Multisensorielle Weltmodellierung (alt) | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition | ||||||||||||||||
Hinterlegt von: | Nissler, Christian | ||||||||||||||||
Hinterlegt am: | 20 Dez 2013 14:14 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 19:53 |
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