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Instance-based object recognition in 3D point clouds using discriminative shape primitives

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Abstract

3D local shapes are a critical cue for object recognition in 3D point clouds. This paper presents an instance-based 3D object recognition method via informative and discriminative shape primitives. We propose a shape primitive model that measures geometrical informativity and discriminativity of 3D local shapes of an object. Discriminative shape primitives of the object are extracted automatically by model parameter optimization. We achieve object recognition from 2.5/3D scenes via shape primitive classification and recover the 3D poses of the identified objects simultaneously. The effectiveness and the robustness of the proposed method were verified on popular instance-based 3D object recognition datasets. The experimental results show that the proposed method outperforms some existing instance-based 3D object recognition pipelines in the presence of noise, varying resolutions, clutter and occlusion.

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Acknowledgements

We would like to thank those institutions: Bologna University for the 3D Scene Dataset; University of Western Australia for the UWA Dataset; Robotics and Computer Vision Lab at Queens University for the Queens Range Image and 3-D Model Dataset. This work was supported by National Science Foundation (NSF) (61275162); the Innovation Foundation of BUAA for Ph.D Graduates; China Scholarship Council funding (201606020087). Thanks for the valuable comments from reviewers.

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  1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, 100191, China

    Jie Zhang & Junhua Sun

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  1. Jie Zhang
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  2. Junhua Sun
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Correspondence to Junhua Sun.

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Zhang, J., Sun, J. Instance-based object recognition in 3D point clouds using discriminative shape primitives. Machine Vision and Applications 29, 285–297 (2018). https://doi.org/10.1007/s00138-017-0885-8

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