A Face-Based Shape Matching Method for IGES Surface Model

Abstract

IGES is a widely used standard for mechanical data exchange. In this paper, we present a new method for the retrieval task of IGES surface model. Based on this method, a novel distinctive face selection strategy is proposed and evaluated. In the training database, each model is treated as a set of disordered faces, and their features are extracted and stored respectively. The Discounted Cumulative Gain (DCG) value of each face is then calculated and stored for later utilization. To retrieve models in the testing database, we first forecast each face's DCG value by searching its most similar face's DCG value in training database, and then the top k faces with highest forecasted DCGs are selected as query input. A greedy algorithm is finally applied to get the total similarity. Experimental results show that our algorithm is superior or at least comparable to some of the most powerful methods in finding parts with similar functionality in most cases.