Skeleton features distribution for 3D object retrieval

Tomoki Hayashi, Benjamin Raynal, Vincent Nozick, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a 3D shape similarity measurement method using the surface skeleton of a voxelized 3D shape. A set of features are extracted from the skeleton by combining the skeleton and the distance map of the subject. The distribution of these extracted features is used to represent the 3D shape. Two 3D shapes can be compared with a similarity score computed from the distance between their respective features distribution. Our method is robust to noise and to partial occlusion. Moreover, it is scale and rotation invariant. We tested and validated our method with various objects with different shape and topology.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
Pages377-380
Number of pages4
Publication statusPublished - 2011 Dec 1
Event12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara, Japan
Duration: 2011 Jun 132011 Jun 15

Publication series

NameProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011

Other

Other12th IAPR Conference on Machine Vision Applications, MVA 2011
CountryJapan
CityNara
Period11/6/1311/6/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Hayashi, T., Raynal, B., Nozick, V., & Saito, H. (2011). Skeleton features distribution for 3D object retrieval. In Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011 (pp. 377-380). (Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011).