Object Recognition in 3D Point Clouds
Using Web Data and Domain Adaptation
The International Journal of Robotics Research
Abstract
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.