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This paper presents an approach to measure 3D similarity by combining two feature vectors. We extract the feature vectors by employing two similarity models: direction vector of surfaces (DVS) and shape histogram of projected volume (SHV). Then we merge the features by two approaches: merging the two original feature vectors and merging computed-distances. Our experiments show that combining two features using either feature merging or distance merging enhances the retrieval performance. Furthermore, we show that employing weighting factor to the merging process implies differently to the retrieval performance, depending on data set distribution. Finally, we introduce an idea of meta feature-vectors which regards the already calculated distances as new feature vectors. Using this approach, a new similarity space might be established, and new distances could be calculated in order to enhance the performance.