FROM 3D MODEL DATA TO SEMANTICS

The semantic-based 3D models retrieval systems have become necessary since the increase of 3D modelsdatabases. In this paper, we propose a new method for the mapping problem between 3D model data andsemantic data involved in semantic based retrieval for 3D models given by polygonal meshes. First, wefocused on extracting invariant descriptors from the 3D models and analyzing them to efficient semanticannotation and to improve the retrieval accuracy. Selected shape descriptors provide a set of termscommonly used to describe visually a set of objects using linguistic terms and are used as semanticconcept to label 3D model. Second, spatial relationship representing directional, topological anddistance relationships are used to derive other high-level semantic features and to avoid the problem ofautomatic 3D model annotation. Based on the resulting semantic annotation and spatial concepts, anontology for 3D model retrieval is constructed and other concepts can be inferred. This ontology is usedto find similar 3D models for a given query model. We adopted the query by semantic example approach,in which the annotation is performed mostly automatically. The proposed method is implemented in our3D search engine (SB3DMR), tested using the Princeton Shape Benchmark Database.