Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation

<p>Over the last years, object detection has become a more and more active field of research in robotics. An important problem in object detection is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google's 3DWarehouse to train an object detection system for 3D point clouds collected by robots navigating through both urban and indoor environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled point clouds and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real-world environments.</p>

http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/scan-domain-... PDF
http://www.cs.washington.edu/homes/kevinlai/publications/lai_ijrr10.bib bibtex