Learning informative point classes for the acquisition of object model maps

—This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on. The feature space comprises a multi-value histogram which characterizes the local geometry around a query point, is pose and sampling density invariant, and can cope well with noisy sensor data. We characterize 3D geometric primitives of interest and describe methods for obtaining discriminating features used in a machine learning algorithm. To validate our approach, we perform an in-depth analysis using different classifiers and show results with both synthetically generated datasets and real-world scans.

• Type: Conference
• Year: 2008
• Where: ICARCV
• Authors: Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Michael Beetz
• Keywords: 3D Point Cloud, ICARCV 2008, Robotics, Robust Feature Point, Sampling Density Invariant