Title: 3D object classification and parameter estimation based on parametric procedural models
Authors: Getto, Roman
Fina, Kenten
Jarms, Lennart
Kuijper, Arjan
Fellner, Dieter W.
Citation: WSCG 2018: full papers proceedings: 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, p. 10-19.
Issue Date: 2018
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2801.pdf
ISBN: 978-80-86943-40-4
ISSN: 2464–4617 (print)
2464–4625 (CD-ROM)
Keywords: procesní model;parametrický model;parametrizace;klasifikace 3D objektů;hluboké učení
Keywords in different language: procedural model;parametric model;parameterization;3D object classification;deep learning
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.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2018: Full Papers Proceedings

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/34620

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