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<dc:title xml:lang="fr">Relevé et consolidation de nuages de points issus de multiples capteurs pour la numérisation 3D du patrimoine</dc:title>
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<dc:subject xml:lang="fr">Recalage global</dc:subject>
<dc:subject xml:lang="fr">Nuages de points hétérogènes</dc:subject>
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<dc:subject xml:lang="fr">Estimation robuste</dc:subject>
<dc:subject xml:lang="en">Digitization techniques</dc:subject>
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<dc:subject xml:lang="en">Global registration</dc:subject>
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<tef:elementdEntree autoriteExterne="027390896" autoriteSource="Sudoc">Analyse multivariée</tef:elementdEntree>
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<dcterms:abstract xml:lang="fr">La numérisation 3D du patrimoine bâti est un procédé qui s’inscrit dans de multiples applications (documentation, visualisation, etc.), et peut tirer profit de la diversité des techniques de mesure disponibles. Afin d’améliorer la complétude et la qualité des livrables, de plus en plus de projets de numérisation s’appuient sur la combinaison de nuages de points provenant de différentes sources. La connaissance des performances propres aux différents capteurs, ainsi que de la qualité de leurs mesures, est alors souhaitable. Par la suite, plusieurs pistes peuvent être explorées en vue d’intégrer des nuages hétérogènes au sein d’un même projet, de leur recalage à la modélisation finale. Une approche pour le recalage simultané de plusieurs nuages de points est exposée dans ces travaux. La gestion de potentielles fautes parmi les observations, ou de bruit de mesure inhérent à certaines techniques de levé, est envisagée à travers l’ajout d’estimateurs robustes dans la méthodologie de recalage.</dcterms:abstract>
<dcterms:abstract xml:lang="en">Three dimensional digitization of built heritage is involved in a wide range of applications (documentation, visualization, etc.), and may take advantage of the diversity of measurement techniques available. In order to improve the completeness as well as the quality of deliverables, more and more digitization projects rely on the combination of data coming from different sensors. To this end, the knowledge of sensor performances along with the quality of the measurements they produce is recommended. Then, different solutions can be investigated to integrate heterogeneous point clouds within a same project, from their registration to the modeling steps. A global approach for the simultaneous registration of multiple point clouds is proposed in this work, where the introduction of individual weights for each dataset is foreseen. Moreover, robust estimators are introduced in the registration framework, in order to deal with potential outliers or measurement noise among the data.</dcterms:abstract>
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