Feature Template based 3D Model Retrieval

Abstract

3D model retrieval has attracted more and more research interests. Lots of shape descriptors have been proposed till now. But during the process of constructing these shape descriptors, feature correlation among models is not considered. In this paper, we propose a simple but very effective method in improving retrieving accuracy by employing correlative information, namely feature template. Feature template is designed to remove such small variation while remaining discriminative features by performing meaning operation of feature vectors. As a result, it makes the feature vector be more robust for better retrieving accuracy. In addition, the feature template can be regarded as a post-processing of existing shape descriptors. Therefore, the proposed method can be used to improve retrieving accuracy for any shape descriptors in the form of feature vector. In experiments, we test the proposed method for several shape descriptors by using a public 3D model database. Comparing with original shape descriptors, our method can greatly improve the retrieval accuracy.