ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 225-231, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
03 Jun 2016
Sven Oesau, Florent Lafarge, and Pierre Alliez INRIA Sophia Antipolis - Méditerranée, France
Keywords: object classification, point cloud processing, machine learning, planar abstraction 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.
Conference paper (PDF, 3407 KB)

Citation: Oesau, S., Lafarge, F., and Alliez, P.: OBJECT CLASSIFICATION VIA PLANAR ABSTRACTION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 225-231,, 2016.

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