3D Part identification based on local shape descriptors

Authors:
Xiaolan Li;Afzal Godil;Asim Wagan
Affiliations:
National Institute of Standards and Technology, Gaithersburg, MD and Zhejiang Gongshang University, Hangzhou, Zhejiang, China;National Institute of Standards and Technology, Gaithersburg, MD;National Institute of Standards and Technology, Gaithersburg, MD
Venue:
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Year:
2008

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Abstract

This paper explores 3D object recognition based on local shape descriptor. 3D object recognition is becoming an increasingly important task in modern applications such as computer vision, CAD/CAM, multimedia, molecular biology, robotics, and so on. Compared with general objects, CAD models contain more complicated structures and subtle local features. It is especially challenging to recognize the CAD model from the point clouds which only contain partial data of the model. We adopt the Bag of Words framework to do the partial-to-global 3D CAD retrieval. In this paper the visual words dictionary is constructed based on the spin image local feature descriptor. The method is tested on the Purdue Engineering Benchmark. Furthermore, several experiments are performed to show how the size of query data and the dissimilarity measurement affect the retrieval results.