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A comparison of geometric and energy-based point cloud semantic segmentation methods

  • Mathieu Dubois
  • , Paola K. Rozo
  • , Alexander Gepperth
  • , O. Fabio A. Gonzalez
  • , David Filliat
  • Universidad Nacional de Colombia

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collectionContribution à une conférenceRevue par des pairs

1 Citation (Scopus)

Résumé

The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall, ground, objects, etc. Several effective solutions have been proposed, mainly based on the recursive decomposition of the point cloud into planes. We compare such a solution to a non-associative MRF method inspired by some recent work in computer vision. The MRF yields interesting results that are however less good than those of a carefully tuned geometric method. Nevertheless, MRF still has some advantages and we suggest some improvements.

langue originaleAnglais
titre2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings
EditeurIEEE Computer Society
Pages88-93
Nombre de pages6
ISBN (imprimé)9781479902637
Les DOIs
étatPublié - 1 janv. 2013
Evénement2013 6th European Conference on Mobile Robots, ECMR 2013 - Barcelona, Espagne
Durée: 25 sept. 201327 sept. 2013

Série de publications

Nom2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings

Une conférence

Une conférence2013 6th European Conference on Mobile Robots, ECMR 2013
Pays/TerritoireEspagne
La villeBarcelona
période25/09/1327/09/13

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