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Transductive Segmentation of Textured Meshes Anne-Laure Chauve, Jean-Philippe Pons, Jean-Yves Audibert, and
 

Summary: Transductive Segmentation of Textured Meshes
Anne-Laure Chauve, Jean-Philippe Pons, Jean-Yves Audibert, and
Renaud Keriven
IMAGINE, ENPC/CSTB/LIGM, Universit´e Paris-Est, France
Abstract. This paper addresses the problem of segmenting a textured
mesh into objects or object classes, consistently with user-supplied seeds.
We view this task as transductive learning and use the flexibility of
kernel-based weights to incorporate a various number of diverse features.
Our method combines a Laplacian graph regularizer that enforces spa-
tial coherence in label propagation and an SVM classifier that ensures
dissemination of the seeds characteristics. Our interactive framework al-
lows to easily specify classes seeds with sketches drawn on the mesh and
potentially refine the segmentation. We obtain qualitatively good seg-
mentations on several architectural scenes and show the applicability of
our method to outliers removing.
1 Introduction
The generalization of digital cameras, the increase in computational power
brought by graphical processors and the recent progress in multi-view recon-
struction algorithms allow to create numerous and costless textured 3D models
from digital photographs. In this work, we address the problem of segmenting

  

Source: Audibert, Jean-Yves - Département d'Informatique, École Normale Supérieure

 

Collections: Computer Technologies and Information Sciences