LAUSR

Abstract With the advance of 3D technology and digital image processing technique, there have been a great number of applications of 3D models, such as virtual reality, computed aided design, and entertainment. Under such circumstance, much research attention has been spent on 3D model retrieval in recent decades. Although extensive research efforts have been dedicated to this task, it is a difficult task to explore the correlation among 3D models, which is the key issue in 3D model retrieval. In this paper, we design and implement a constructive-learning for cross-model correlation algorithm for 3D model retrieval. In this method, we first extract view features from multi-views of 3D models. To exploit the cross-model correlation, we formulate the correlation of 3D models in a hypergraph structure, where both the vertex correlation and the edge correlation are simultaneously learned in a constructive-learning process. Then, the correlation of each model to the query can be used for retrieval. To justify the performance of our proposed algorithm, we have implemented the method and tested on two datasets. We have compared it with recent state-of-the-art methods, and the results have shown superior performance of our proposed method.