3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching

Avinash Sharma 1 Radu Horaud 1 Diana Mateus 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this book chapter we address the problem of 3D shape registration and we propose a novel technique based on spectral graph theory and probabilistic matching. Recent advancement in shape acquisition technology has led to the capture of large amounts of 3D data. Existing real-time multi-camera 3D acquisition methods provide a frame-wise reliable visual-hull or mesh representations for real 3D animation sequences The task of 3D shape analysis involves tracking, recognition, registration, etc. Analyzing 3D data in a single framework is still a challenging task considering the large variability of the data gathered with different acquisition devices. 3D shape registration is one such challenging shape analysis task. The main contribution of this chapter is to extend the spectral graph matching methods to very large graphs by combining spectral graph matching with Laplacian embedding. Since the embedded representation of a graph is obtained by dimensionality reduction we claim that the existing spectral-based methods are not easily applicable. We discuss solutions for the exact and inexact graph isomorphism problems and recall the main spectral properties of the combinatorial graph Laplacian; We provide a novel analysis of the commute-time embedding that allows us to interpret the latter in terms of the PCA of a graph, and to select the appropriate dimension of the associated embedded metric space; We derive a unit hyper-sphere normalization for the commute-time embedding that allows us to register two shapes with different samplings; We propose a novel method to find the eigenvalue-eigenvector ordering and the eigenvector sign using the eigensignature (histogram) which is invariant to the isometric shape deformations and fits well in the spectral graph matching framework, and we present a probabilistic shape matching formulation using an expectation maximization point registration algorithm which alternates between aligning the eigenbases and finding a vertex-to-vertex assignment.
Type de document :
Chapitre d'ouvrage
Olivier Lezoray and Leo Grady. Image Processing and Analysing With Graphs: Theory and Practice, CRC Press, pp.441-474, 2012
Liste complète des métadonnées

Littérature citée [49 références]  Voir  Masquer  Télécharger


https://hal.inria.fr/inria-00590273
Contributeur : Team Perception <>
Soumis le : mercredi 10 août 2011 - 10:39:15
Dernière modification le : jeudi 11 janvier 2018 - 06:21:59
Document(s) archivé(s) le : lundi 5 décembre 2016 - 04:06:11

Fichiers

Sharma_main.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00590273, version 2

Collections

Citation

Avinash Sharma, Radu Horaud, Diana Mateus. 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching. Olivier Lezoray and Leo Grady. Image Processing and Analysing With Graphs: Theory and Practice, CRC Press, pp.441-474, 2012. 〈inria-00590273v2〉

Partager

Métriques

Consultations de la notice

1330

Téléchargements de fichiers

3006