Hierarchical Normal Space Sampling to speed up point cloud coarse matching
dc.contributor.author
dc.date.accessioned
2013-12-03T09:22:55Z
dc.date.available
2013-12-03T09:22:55Z
dc.date.issued
2012
dc.identifier.issn
0167-8655
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dc.description.abstract
Point cloud matching is a central problem in Object Modeling with applications in Computer Vision and Computer Graphics. Although the problem is well studied in the case when an initial estimate of the relative pose is known (fine matching), the problem becomes much more difficult when this a priori knowledge is not available (coarse matching). In this paper we introduce a novel technique to speed up coarse matching algorithms for point clouds. This new technique, called Hierarchical Normal Space Sampling (HNSS), extends Normal Space Sampling by grouping points hierarchically according to the distribution of their normal vectors. This hierarchy guides the search for corresponding points while staying free of user intervention. This permits to navigate through the huge search space taking advantage of geometric information and to stop when a sufficiently good initial pose is found. This initial pose can then be used as the starting point for any fine matching algorithm. Hierarchical Normal Space Sampling is adaptable to different searching strategies and shape descriptors. To illustrate HNSS, we present experiments using both synthetic and real data that show the computational complexity of the problem, the computation time reduction obtained by HNSS and the application potentials in combination with ICP
dc.description.sponsorship
This work has been supported by FP7-ICT-2011-7 projects: PANDORA Persistent Autonomy through Learning, Adaptation, Observation and Re-planning (Ref 288273) funded by the European Commission and RAIMON Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref CTM2011-29691-C02-02) funded by the Spanish Ministry of Science and Innovation
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation
info:eu-repo/grantAgreement/MICINN//CTM2011-29691-C02-02/ES/ROBOT AUTONOMO SUBMARINO PARA LA INSPECCION Y MONITORIZACION DE EXPLOTACIONES DE ACUICULTURA MARINA/
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Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.patrec.2012.07.006
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© Pattern Recognition Letters, 2012, vol. 33, núm. 16, p. 2127-2133
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Articles publicats (D-ATC)
dc.rights
Tots els drets reservats
dc.subject
dc.title
Hierarchical Normal Space Sampling to speed up point cloud coarse matching
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/embargoedAccess
dc.embargo.terms
Cap
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2026-01-01
dc.relation.projectID
info:eu-repo/grantAgreement/EC/FP7/288273/EU/Persistent Autonomy through Learning, Adaptation, Observation and Re-planning/PANDORA
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
016262
dc.contributor.funder
dc.relation.FundingProgramme
dc.relation.ProjectAcronym