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ICRA
2009
IEEE

Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields

10 years 1 months ago
Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields
Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performance increase is often at the cost of time consuming, memory intensive learning and slow inference at testing time. Structured prediction for 3-D point cloud classification is one example of such an application. In this paper we present two contributions. First we show how efficient learning of a random field with higher-order cliques can be achieved using subgradient optimization. Second, we present a context approximation using random fields with high-order cliques designed to make this model usable online, onboard a mobile vehicle for environment modeling. We obtained results with the mobile vehicle on a variety of terrains, at 1/3 Hz for a map 25
Daniel Munoz, Nicolas Vandapel, Martial Hebert
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICRA
Authors Daniel Munoz, Nicolas Vandapel, Martial Hebert
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