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A framework for the objective evaluation of segmentation algorithms using a ground-truth of human segmented 3D-models

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  • Additional Information
    • Contributors:
      FOX MIIRE (LIFL); Laboratoire d'Informatique Fondamentale de Lille (LIFL); Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS); Institut TELECOM/TELECOM Lille1; Institut Mines-Télécom Paris (IMT); Geometry Processing and Constrained Optimization (M2DisCo); Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS); Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS); ANR-07-MDCO-0015,MADRAS,3D Models And Dynamic models Representation And Segmentation(2007)
    • Publication Information:
      HAL CCSD
    • Publication Date:
      2009
    • Collection:
      Université de Lille 3 - Sciences Humaines et Sociales: HAL
    • Subject Terms:
      Beijing; China
    • Abstract:
      International audience ; In this paper, we present an evaluation method of 3D-mesh segmentation algorithms based on a ground-truth corpus. This corpus is composed of a set of 3D-models grouped in different classes (animals, furnitures, etc.) associated with several manual segmentations produced by human observers. We define a measure that quantifies the consistency between two segmentations of a 3D-model, whatever their granularity. Finally, we propose an objective quality score for the automatic evaluation of 3D-mesh segmentation algorithms based on these measures and on the ground-truth corpus. Thus the quality of segmentations obtained by automatic algorithms is evaluated in a quantitative way thanks to the quality score, and on an objective basis thanks to the ground- truth corpus. Our approach is illustrated through the evaluation of two recent 3D-mesh segmentation methods.
    • Online Access:
      https://hal.science/hal-00667994
      https://hal.science/hal-00667994/document
      https://hal.science/hal-00667994/file/benhabilesSMI2009.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.71048BDE