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3D-mesh segmentation: automatic evaluation and a new learning-based method

Halim Benhabiles 1, 2
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : In this thesis, we address two main problems namely the quantitative evaluation of mesh segmentation algorithms and learning mesh segmentation by exploiting the human factor. We propose the following contributions: - A benchmark dedicated to the evaluation of mesh segmentation algorithms. The benchmark includes a human-made ground-truth segmentation corpus and a relevant similarity metric that quantifies the consistency between these ground-truth segmentations and automatic ones produced by a given algorithm on the same models. Additionally, we conduct extensive experiments including subjective ones to respectively demonstrate and validate the relevance of our benchmark. - A new learning mesh segmentation algorithm. A boundary edge function is learned, using multiple geometric criteria, from a set of human segmented training meshes and then used, through a processing pipeline, to segment any input mesh. We show, through a set of experiments using different benchmarks, the performance superiority of our algorithm over the state-of-the-art. We present also an application of our segmentation algorithm for kinematic skeleton extraction of dynamic 3D-meshes.
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  • HAL Id : tel-00834344, version 1

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Halim Benhabiles. 3D-mesh segmentation: automatic evaluation and a new learning-based method. Computer Vision and Pattern Recognition [cs.CV]. Université des Sciences et Technologie de Lille - Lille I, 2011. English. ⟨tel-00834344⟩

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