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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings
PublisherIEEE Computer Society
Pages88-93
Number of pages6
ISBN (Print)9781479902637
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 6th European Conference on Mobile Robots, ECMR 2013 - Barcelona, Spain
Duration: 25 Sept 201327 Sept 2013

Publication series

Name2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings

Conference

Conference2013 6th European Conference on Mobile Robots, ECMR 2013
Country/TerritorySpain
CityBarcelona
Period25/09/1327/09/13

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