3D Object Retrieval based on Correlation of Multi-view Image Local Feature

Abstract

In this paper, we propose a new feature vector for 3D object retrieval, which we call Local Feature Correlation Descriptor (LCoD). Given a 3D object, we first render depth-buffer images from multiple viewpoints. We then extract local features from each depth-buffer image. For every depth-buffer image, we compute the correlation matrix of local features, and define the vector as LCoD, which is obtained by the elements of the correlation matrix. Our experiments on the Princeton Shape Benchmark show that LCoD achieves the First Tier of 0.4708, which exhibits higher search performance than conventional techniques.