Skip to Main content Skip to Navigation
Journal articles

A parts-based approach for automatic 3D-shape categorization using belief functions

Hedi Tabia 1 Mohamed Daoudi 2, 3 Jean-Philippe Vandeborre 2, 3 Olivier Colot 1
1 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : Grouping 3D-objects into (semantically) meaningful categories is a challenging and important problem in 3D-mining and shape processing. Here, we present a novel approach to categorize 3D-objects. The method described in this paper, is a belief function based approach and consists of two stages. The training stage, where 3D-objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca- Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models.
Document type :
Journal articles
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00794042
Contributor : Jean-Philippe Vandeborre <>
Submitted on : Sunday, February 24, 2013 - 10:53:18 PM
Last modification on : Thursday, March 4, 2021 - 6:24:03 PM
Long-term archiving on: : Saturday, May 25, 2013 - 4:07:58 AM

File

ACM-TIST-V4N2-TIST-2010-07-017...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00794042, version 1

Citation

Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre, Olivier Colot. A parts-based approach for automatic 3D-shape categorization using belief functions. ACM Transactions on Intelligent Systems and Technology, ACM, 2013, 4 (2), pp.33:1-33:16. ⟨hal-00794042⟩

Share

Metrics

Record views

403

Files downloads

572