Three-dimensional model databases used around the Web become larger; therefore, the development of retrieval systems for such databases is required. In this paper, we propose a new 3D shape descriptor and a web based search engine for 3D models given by polygonal meshes. The main idea is to reconstruct a 3D closed curve that represents the 3D object and to extract feature vectors from it. We combine two new descriptors the area and the dot product descriptors that describe the reconstructed 3D closed curve in order to define the 3D curve analysis descriptor. The proposed method is robust for noise, compared to other descriptors known in the literature and tested using the Princeton shape benchmark database via the search engine.

The size of 3-D data stored around the Web has become bigger. Therefore the development of recognition applications and retrieval systems of 3-D models is important. In this paper we propose a new scheme to measure similarity between 3-D models. The main idea is to reconstruct a 3-D closed curve that represents a 3-D model given by a polygonal mesh, and to extract a signature from this 3-D closed curve using the Fourier series. The proposed descriptor needs CPCA (Continuous Principal Component Analysis) to align 3-D models into a canonical position. The feature vectors constructed using this method, named FSD (Fourier Series Descriptor) are invariants under rigid transformations composed of translation, rotation, flipping and scale; robust to noise and level of detail. A 3-D polygonal mesh model serves as a query for search by shape similarity in a large collection of 3-D models database using an interactive 3-D search engine.

Retrieval systems for 3D objects are required because 3D databases used around the web are growing. In this paper, we propose a visual similarity based search engine for 3D objects. The system is based on a new representation of 3D objects given by a 3D closed curve that captures all information about the surface of the 3D object. We propose a new 3D descriptor, which is a combination of three signatures of this new representation, and we implement it in our interactive web based search engine. Our method is compared to some state of the art methods, tested using the Princeton-Shape Benchmark as a large database of 3D objects. The experimental results show that the enhanced curve analysis descriptor performs well.

The size of 3D data stored around the web has become bigger. Therefore the development of recognition applications and retrieval systems of 3D models is important. This paper deals with invariants for 3D models recognition. Thus under general affine transform we propose in a first time determinants of three points to realize invariance under affinity. To solve starting point problem we needs Fourier Series (FS) to extract affine invariant descriptors, called Fourier Series Descriptor (FSD). The difference between first and second approaches: in first approach determinants are computed on cartesian coordinates directly while in the second one determinants are computed from FS coefficients to eliminate dependency on starting point. The FS are also applied on 2D slices to generate affine invariants for 3D volume. FS can be computed based on hole points of volume, but this technique. The principal advantages of proposed approaches is the possibility to handle affine transform and 3D volume. Two types of 3D objects are used in the experimentations: mesh and volume, the Princeton Shape Benchmarek (PSB) is also used to test our descriptor based on FSD

On the Web and on informatics systems, 3D virtual worlds used become big both in number and in size. Therefore, we propose in this paper a new method for retrieving 3D virtual worlds based on the semantics and the content. Firstly, we propose a new classified database of 3D virtual worlds given in VRML format. To achieve the semantic method, we construct an ontology that describes virtual worlds in various aspects including their contents (3D objects building a virtual world) and information about their contents (authors, file format, etc). This ontology is presented by OWL, the W3C recommended language. So as to extract desired 3D scenes from the proposed database we use the SPARQL query language. We propose finally a shape based method for searching desired virtual worlds by content in this database. This method is based on a new distance (metric) that define the similarity between virtual worlds. This method is evaluated using the recall vs. precision curves.