Retrieving 3D Model Using Compound-Eye Visual Representation

This paper describes a novel method for retrieving 3D models. Following the principle of compound-eye vision, the proposed method represents a 3D model as a spherical image, and discriminates different 3D models using their corresponding spherical images. Meanwhile, by borrowing the concept of the Scale-Invariant Feature Transform (SIFT) algorithm, we design a feature extraction algorithm, named Spherical-SIFT, for extracting the salient local features on spherical images. Moreover, the Bag-of-Features approach is employed so as to achieve efficient comparison of different 3D models. The experimental results show the superior performance of our method over pervious methods.