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Hierarchical Normal Space Sampling to speed up point cloud coarse matching

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

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

© Pattern Recognition Letters, 2012, vol. 33, núm. 16, p. 2127-2133

Elsevier

Author: Díez Donoso, Santiago
Martí Bonmatí, Joan
Salvi, Joaquim
Date: 2012
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
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
Format: application/pdf
ISSN: 0167-8655
Document access: http://hdl.handle.net/10256/8645
Language: eng
Publisher: Elsevier
Collection: MICINN/PN 2012-2014/CTM2011-29691-C02-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.patrec.2012.07.006
Articles publicats (D-ATC)
info:eu-repo/grantAgreement/EC/FP7/288273
Is part of: © Pattern Recognition Letters, 2012, vol. 33, núm. 16, p. 2127-2133
Rights: Tots els drets reservats
Subject: Visió per ordinador
Computer vision
Reconeixement de formes (Informàtica)
Pattern recognition systems
Imatges -- Processament
Image processing
Title: Hierarchical Normal Space Sampling to speed up point cloud coarse matching
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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