Abstract
In this article we propose simple descriptor for the purposes of 3D objects recognition and classification. Princeton Shape Benchmark 2004 is used for testing the proposed descriptor. Small size (512b) of the proposed descriptor and short generation and comparison times combine with relatively high recognition abilities. Surprisingly, we found that despite its simplicity and the small size the proposed descriptor took the first place in “coarser” classification test, where all 3D models were divided into 6 large classes: buildings, household, plants, animals, furniture, vehicles and a miscellaneous class not included in averaged retrieval results.
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Ivanko, E., Perevalov, D. (2005). Q-Gram Statistics Descriptor in 3D Shape Classification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_41
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DOI: https://doi.org/10.1007/11552499_41
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