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Proceedings Paper

3D object recognition based on improved point cloud descriptors
Author(s): Weiwei Wen; Gongjian Wen; Bingwei Hui; Shaohua Qiu
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Paper Abstract

For 3D object recognition, a discriminative point cloud descriptor is required to represent the object. The existing global descriptors encode the whole object into a vector but they are sensitive to occlusion. On the contrary, the local descriptors encode only a small neighbor of a key point and are more robust to occlusion, but many objects have the same local surface. This paper presents a novel mixture method which segments a point cloud into multiple subparts to overcome the above shortcomings. In offline training stage, we propose to build up the model library that integrates both global and local surface of partial point clouds. In online recognition stage, the scene objects are represented by its subparts, and a voting scheme is performed for the recognition of scene objects. Experimental results on public datasets show that the proposed method promotes the recognition performance significantly compared to the conventional global and local descriptors.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060O (9 August 2018); doi: 10.1117/12.2503095
Show Author Affiliations
Weiwei Wen, National Univ. of Defense Technology (China)
Gongjian Wen, National Univ. of Defense Technology (China)
Bingwei Hui, National Univ. of Defense Technology (China)
Shaohua Qiu, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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