3-D Object Classification & Retrieval problem can be stated as identifying a correct class or retrieve relevant objects for a query object. In the past years, researchers developed several useful signatures to describe 3-D objects in a compact way. They focused on global description, graph description and local description. However, most of these signatures cannot handle a generic database well because of their limitations on differentiating 3-D objects in different shapes, poses and surface properties. My research is aiming to develop a sophisticated signature and a complete classification and retrieval scheme to produce high retrieval performance and classification accuracy on a generic database.
Global signatures such as    try to handle capture the global shape basis in a 3-D object but lose the details. Local signature such as  starts from local salient points and then builds up a statistical signature for an entire mesh but it is not robust under large shape variance. Graph signatures extract the topological information from a mesh and analyze it but only effective to limited cases. Our idea is going to conquer the limitations from the previous researches and design a natural description for 3-D objects, which highly complies with human perception.
Challenges will be conquered in the future.
- Ability to differentiate and group objects with different and similar shapes
- Robustness under large pose changes
- Adaptiveness to variance in surface properties
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