Attention Based Active 3D Point Cloud Segmentation
Abstract
In this paper we present a framework for the segmentation of multiple objects from a 3D point cloud. We extend traditional image segmentation techniques into a full 3D representation. The proposed technique relies on a state-of-the-art min-cut framework to perform a fully 3D global multi-class labeling in a principled manner. Thereby, we extend our previous work in which a single object was actively segmented from the background. We also examine several seeding methods to bootstrap the graphical model-based energy minimization and these methods are compared over challenging scenes. All results are generated on real-world data gathered with an active vision robotic head. We present quantitive results over aggregate sets as well as visual results on specific examples.
BibTeX
@conference{Johnson-Roberson-2010-130242,author = {M. Johnson-Roberson and Jeannette Bohg and Marten Bjorkman and Danica Kragic},
title = {Attention Based Active 3D Point Cloud Segmentation},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2010},
month = {October},
pages = {1165 - 1170},
}