Efficient 3-D model search and retrieval using Generalized 3-D Radon Transforms

Measuring the similarity between three-dimensional (3-D) objects is a challenging problem, with applications in computer vision, molecular biology, computer graphics, and many other areas. This paper describes a novel method for 3-D model content-based search based on the 3-D Generalized Radon Transform and a querying by-3-D-model approach. A set of descriptor vectors is extracted using the Radial Integration Transform (RIT) and the Spherical Integration Transform (SIT), which represent significant shape characteristics. After the proper alignment of the models, descriptor vectors are produced which are invariant in terms of translation, scaling and rotation. Experiments were performed using three different databases and comparing the proposed method with those most commonly cited in the literature. Experimental results show that the proposed method is adequately satisfactory in terms of both precision versus recall and time needed for retrieval, and that it can be used for 3-D model search and retrieval in a highly efficient manner.