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3D Object Classification and Parameter Estimation based on Parametric Procedural Models

Getto, Roman ; Fina, Kenten ; Jarms, Lennart ; Kuijper, Arjan ; Fellner, Dieter W. (2018)
3D Object Classification and Parameter Estimation based on Parametric Procedural Models.
International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG). Plzen, Czech Republic (2018)
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Classifying and gathering additional information about an unknown 3D objects is dependent on having a large amount of learning data. We propose to use procedural models as data foundation for this task. In our method we (semi-)automatically define parameters for a procedural model constructed with a modeling tool. Then we use the procedural models to classify an object and also automatically estimate the best parameters. We use a standard convolutional neural network and three different object similarity measures to estimate the best parameters at each degree of detail. We evaluate all steps of our approach using several procedural models and show that we can achieve high classification accuracy and meaningful parameters for unknown objects.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2018
Autor(en): Getto, Roman ; Fina, Kenten ; Jarms, Lennart ; Kuijper, Arjan ; Fellner, Dieter W.
Art des Eintrags: Bibliographie
Titel: 3D Object Classification and Parameter Estimation based on Parametric Procedural Models
Sprache: Englisch
Publikationsjahr: 2018
Ort: Plzen
Buchtitel: WSCG 2018. Full Papers Proceedings
Reihe: Computer Science Research Notes (CSRN)
Band einer Reihe: 2801
Veranstaltungstitel: International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)
Veranstaltungsort: Plzen, Czech Republic
Veranstaltungsdatum: 2018
Kurzbeschreibung (Abstract):

Classifying and gathering additional information about an unknown 3D objects is dependent on having a large amount of learning data. We propose to use procedural models as data foundation for this task. In our method we (semi-)automatically define parameters for a procedural model constructed with a modeling tool. Then we use the procedural models to classify an object and also automatically estimate the best parameters. We use a standard convolutional neural network and three different object similarity measures to estimate the best parameters at each degree of detail. We evaluate all steps of our approach using several procedural models and show that we can achieve high classification accuracy and meaningful parameters for unknown objects.

Freie Schlagworte: Procedural modeling, Parametric modeling, Parameterization, 3D Objects, Classifications, Deep learning
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing
Hinterlegungsdatum: 10 Jul 2019 12:13
Letzte Änderung: 04 Feb 2022 12:39
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