Unsupervised learning of 3D object models from partial views

Authors:
Michael Ruhnke;Bastian Steder;Giorgio Grisetti;Wolfram Burgard
Affiliations:
Autonomous Systems Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany;Autonomous Systems Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany;Autonomous Systems Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany;Autonomous Systems Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models.