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Visual Similarity based 3D Shape Retrieval Using Bag-of-Features



Zhouhui Lian, Afzal A. Godil, Xianfang Sun


This paper develops a novel 3D shape retrieval method, which uses Bag-of-Features and an efficient multi-view shape matching scheme. In our approach, a properly normalized object is first described by a set of depth-buffer views captured on the surrounding vertices of a given unit geodesic sphere. We then represent each view as a word histogram generated by the vector quantization of the view s salient local features. The dissimilarity between two 3D models is measured by the minimum distance of their all (24) possible matching pairs. This paper also investigates several critical issues including the influence of the number of views, codebook, training data, and distance function. Experiments on four commonly-used benchmarks demonstrate that: 1) Our approach obtains superior performance in searching for rigid models. 2) The local feature and global feature based methods are somehow complementary. Moreover, a linear combination of them significantly outperforms the state-of-the-art in terms of retrieval accuracy.
Conference Dates
June 21-23, 2010
Conference Location
Conference Title
IEEE Shape Modeling International 2010 (SMI'10)


3D Shape Retrieval, Bag-of-Features, SIFT, Local Descriptor


Lian, Z. , Godil, A. and Sun, X. (2010), Visual Similarity based 3D Shape Retrieval Using Bag-of-Features, IEEE Shape Modeling International 2010 (SMI'10), Aix-en-Provence, -1, [online], (Accessed May 23, 2024)


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Created June 21, 2010, Updated February 19, 2017