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Thesis of

Halim Benhabiles

Tuesday 18 October 2011

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

Directeur : Mohamed Daoudi, Professeur, TELECOM Lille 1
Co-directeurs : Guillaume Lavoué, Maître de Conférences, INSA-Lyon
M. Jean-Philippe Vandeborre, Maître de Conférences, TELECOM Lille 1
Rapporteurs : Christophe Rosenberger, Professeur, ENSICAEN
Michela Spagnuolo, Senior Researcher, IMATI-GE / CNR (Italie)
Examinateurs : Florent Dupont, Professeur, Université Lyon 1
François Goulette, Maître-Assistant (HDR), MINES ParisTEch
Philippe Preux, Professeur, Université Lille 3

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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