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

Roman Getto, Kenten Fina, Lennart Jarms, Arjan Kuijper, Dieter Fellner

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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.
Originalspracheenglisch
TitelWSCG 2018
Redakteure/-innenVaclav Skala
Herausgeber (Verlag)University of West Bohemia
ISBN (Print)978-80-86943-40-4
PublikationsstatusVeröffentlicht - 2018
Veranstaltung26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision - Pilsen, Tschechische Republik
Dauer: 28 Mai 20181 Jun 2018

Publikationsreihe

NameComputer Science Research Notes
Band2801

Konferenz

Konferenz26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision
KurztitelWSCG 2018
LandTschechische Republik
OrtPilsen
Zeitraum28/05/181/06/18

Schlagwörter

    Fields of Expertise

    • Information, Communication & Computing

    Dieses zitieren

    Getto, R., Fina, K., Jarms, L., Kuijper, A., & Fellner, D. (2018). 3D Object Classification and Parameter Estimation based on Parametric Procedural Models. in V. Skala (Hrsg.), WSCG 2018 (Computer Science Research Notes ; Band 2801). University of West Bohemia.