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Directional Associative Markov Network for 3-D Point Cloud Classification
D. Munoz, N. Vandapel, and M. Hebert
Fourth International Symposium on 3D Data Processing, Visualization and Transmission, June, 2008.

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Abstract

In this paper we address the problem of automated three dimensional point cloud interpretation. This problem is important for various tasks from environment modeling to obstacle avoidance for autonomous robot navigation. In addition to locally extracted features, classifiers need to utilize contextual information in order to perform well. A popular approach to account for context is to utilize the Markov Random Field framework. One recent variant that has successfully been used for the problem considered is the Associative Markov Network (AMN). We extend the AMN model to learn directionality in the clique potentials, resulting in a new anisotropic model that can be efficiently learned using the subgradient method. We validate the proposed approach using data collected from different range sensors and show better performance against standard AMN and Support Vector Machine algorithms.

Notes

Sponsor: Army Research Laboratory
Grant ID: DAAD19-01-209912

Associated centers: VASC and FRC
Associated lab/group: NavLab
Associated project: CTA Robotics

Text Reference

D. Munoz, N. Vandapel, and M. Hebert, "Directional Associative Markov Network for 3-D Point Cloud Classification," Fourth International Symposium on 3D Data Processing, Visualization and Transmission, June, 2008.

BibTeX Reference

@inproceedings{Munoz_2008_6074,
   author = "Daniel Munoz and Nicolas Vandapel and Martial Hebert",
   title = "Directional Associative Markov Network for 3-D Point Cloud Classification",
   booktitle = "Fourth International Symposium on 3D Data Processing, Visualization and Transmission",
   month = "June",
   year = "2008"
}