Virtual Boutique: a 3D modeling and content-based management approach to e-commerce

The Virtual Boutique is made out of three modules: the decor, the market and the search engine. The decor is the physical space occupied by the Virtual Boutique. It can reproduce any existing boutique. For this purpose, photogrammetry is used. A set of pictures of a real boutique or space is taken and a virtual 3D representation of this space is calculated from them. Calculations are performed with software developed at NRC. This representation consists of meshes and texture maps. The camera used in the acquisition process determines the resolution of the texture maps. Decorative elements are added like painting, computer generated objects and scanned objects. The objects are scanned with laser scanner developed at NRC. This scanner allows simultaneous acquisition of range and color information based on white laser beam triangulation. The second module, the market, is made out of all the merchandises and the manipulators, which are used to manipulate and compare the objects. The third module, the search engine, can search the inventory based on an object shown by the customer in order to retrieve similar objects base don shape and color. The items of interest are displayed in the boutique by reconfiguring the market space, which mean that the boutique can be continuously customized according to the customer's needs. The Virtual Boutique is entirely written in Java 3D and can run in mono and stereo mode and has been optimized in order to allow high quality rendering.

[1]  Jarek Rossignac,et al.  Interactive exploration of distributed 3D databases over the Internet , 1998, Proceedings. Computer Graphics International (Cat. No.98EX149).

[2]  Daphna Weinshall,et al.  Automatic hierarchical classification of silhouettes of 3D objects , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[3]  S. El-Hakim A PRACTICAL APPROACH TO CREATING PRECISE AND DETAILED 3D MODELS FROM SINGLE AND MULTIPLE VIEWS , 2000 .

[4]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[5]  Detmar W. Straub,et al.  A conceptual investigation of the e-commerce industry , 2000, CACM.

[6]  June-Ho Yi,et al.  Model-Based 3D Object Recognition Using Bayesian Indexing , 1998, Comput. Vis. Image Underst..

[7]  Sharad Mehrotra,et al.  Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces , 2000, VLDB.

[8]  Marc Rioux,et al.  Nefertiti: A tool for 3-D shape databases management , 1999 .

[9]  Yanxi Liu,et al.  A classification based similarity metric for 3D image retrieval , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).