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A Study on the Robustness of Shape Descriptors to common scanning artifacts

Registration is a fundamental problem in a myriad of applications ranging from heritage reconstruction to industrial applications. Descriptors are an important part of the registration pipeline as well as a very active research field. However, the sets used to illustrate descriptor performance have often undergone several preprocessing steps such as noise filtering, hole filling or outlier removal. These steps simplify the problem but are not readily available in many applications. In this paper we compare the performances of 4 state of the art shape descriptors: SHOT [1], Spin Image [2], FPFH [3] and 3DSC [4]. Experiments were carried out with real as well as synthetic data paying special attention to issues commonly present in real data (noise, outliers and low overlap). The method obtaining a best result overall is SHOT, based mostly on the results with synthetic data. Experiments with real data showed how state of the art descriptors are not yet able to produce optimal results in the most challenging scenarios

This work has been supported by the FP7-ICT-2011-7 project PANDORA-Persistent Autonomy through Learning, Adaptation, Observation and Replanning (Ref 288273) funded by the European Commission and the project RAIMON-Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref CTM2011-29691-C02-02) funded by the Ministry of Economy and Competitiveness of the Spanish Government

© Machine Vision Applications (MVA), 2015 14th IAPR International Conference on, 2015, p.522-525

Institute of Electrical and Electronics Engineers (IEEE)

Author: Roure Garcia, Ferran
Díez Donoso, Santiago
Lladó Bardera, Xavier
Forest Collado, Josep
Pribanic, Tomislav
Salvi, Joaquim
Date: 2015
Abstract: Registration is a fundamental problem in a myriad of applications ranging from heritage reconstruction to industrial applications. Descriptors are an important part of the registration pipeline as well as a very active research field. However, the sets used to illustrate descriptor performance have often undergone several preprocessing steps such as noise filtering, hole filling or outlier removal. These steps simplify the problem but are not readily available in many applications. In this paper we compare the performances of 4 state of the art shape descriptors: SHOT [1], Spin Image [2], FPFH [3] and 3DSC [4]. Experiments were carried out with real as well as synthetic data paying special attention to issues commonly present in real data (noise, outliers and low overlap). The method obtaining a best result overall is SHOT, based mostly on the results with synthetic data. Experiments with real data showed how state of the art descriptors are not yet able to produce optimal results in the most challenging scenarios
This work has been supported by the FP7-ICT-2011-7 project PANDORA-Persistent Autonomy through Learning, Adaptation, Observation and Replanning (Ref 288273) funded by the European Commission and the project RAIMON-Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref CTM2011-29691-C02-02) funded by the Ministry of Economy and Competitiveness of the Spanish Government
Format: application/pdf
ISBN: 978-4-9011-2214-6
Document access: http://hdl.handle.net/10256/14123
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: MICINN/PN 2012-2014/CTM2011-29691-C02-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/MVA.2015.7153245
Articles publicats (D-ATC)
info:eu-repo/grantAgreement/EC/FP7/288273
Is part of: © Machine Vision Applications (MVA), 2015 14th IAPR International Conference on, 2015, p.522-525
Rights: Tots els drets reservats
Subject: Visió per ordinador
Computer vision
Visualització tridimensional (Informàtica)
Three-dimensional display systems
Title: A Study on the Robustness of Shape Descriptors to common scanning artifacts
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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