Object classification via planar abstraction

Sven Oesau 1 Florent Lafarge 1 Pierre Alliez 1
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors.
Type de document :
Communication dans un congrès
ISPRS congress, Jul 2016, Prague, Czech Republic
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Contributeur : Florent Lafarge <>
Soumis le : jeudi 19 mai 2016 - 17:10:09
Dernière modification le : jeudi 11 janvier 2018 - 16:36:01

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Sven Oesau, Florent Lafarge, Pierre Alliez. Object classification via planar abstraction. ISPRS congress, Jul 2016, Prague, Czech Republic. 〈hal-01318637〉

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