Three dimensional object representations have become an integral part of modern computer graphic applications such as computer-aided design, game development and audio-visual production. At the Meanwhile, the 3D data has also become extremely common in fields such as computer vision, computation geometry, molecular biology and medicine. This is due to the rapid evolution of graphics hardware and software development, particularly the availability of low cost 3D scanners which has greatly facilitated 3D model acquisition, creation and manipulation. Content-based search is a necessary solution for structuring, managing these multimedia data, and browsing within these data collections. In this context, we are looking for a system that can automatically retrieve the 3D-models visually similar to a requested 3D-object. Existing solutions for 3D-shape retrieval and classification suffer from high variability towards shape-preserving transformations like affine or isometric transformations (non-rigid transformations). In this context, the aim of my research is to develop a system that can automatically retrieve quickly and with precision 3D models visually similar to a 3D-object query. The system has to be robust to non-rigid transformation that a shape can undergo.During my PhD thesis: We havedeveloped a novel approach to match 3D objects in the presence of non-rigid transformation and partially similar models. We have proposed to use a new representation of 3D-surfaces using 3D curves extracted around feature points. Tools from shape analysis of curves are applied to analyze and to compare curves of two 3D-surfaces. We have used the belief functions, as fusion technique, to define a global distance between 3D-objects. We have also experimented this technique in the retrieval and classification tasks. We have proposed the use of Bag of Feature techniques in 3D-object retrieval and classification.
