Author: Xiaqing Pan and C.-C. Jay Kuo

Research Problem

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.

Main Ideas

Global signatures such as [1] [2] [3] try to handle capture the global shape basis in a 3-D object but lose the details. Local signature such as [4] 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.

Fig. 1

Future Challenges

Challenges will be conquered in the future.

  1. Ability to differentiate and group objects with different and similar shapes
  2. Robustness under large pose changes
  3. Adaptiveness to variance in surface properties


  • [1] Osada R, Funkhouser T, Chazelle B, Dobkin D (2002) Shape distributions. ACM Trans Graph 21(4):807–832
  • [2] Shen Y-T, Chen D-Y, Tian X-P, Ouhyoung M (2003) 3D model search engine based on lightfield descriptors. In: Proc. eurographics 2003
  • [3] Kazhdan M, Funkhouser T, Rusinkiewicz S (2003) Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Proc. Symposium on geometry processing
  • [4] Alexander M. Bronstein, Michael M. Bronstein, Leonidas J. Guibas, and Maks Ovsjanikov. 2011. Shape google: Geometric words and expressions for invariant shape retrieval. ACM Trans. Graph.30, 1, Article 1 (February 2011), 20 pages.
  • [5] R. Toldo, U. Castellani, and A. Fusiello. 2009. Visual vocabulary signature for 3D object retrieval and partial matching. In Proceedings of the 2nd Eurographics conference on 3D Object Retrieval(EG 3DOR’09)