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
2 LIFL - FOX MIIRE
3 Institut TELECOM/TELECOM Lille1

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

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, 2013, 4 (2), pp.33:1-33:16. ⟨hal-00794042⟩