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Novel methods for noisy 3D point cloud based object recognition

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Abstract

3D point cloud based object recognition becomes increasingly important in the last few years, as the widely use of point cloud over the low-cost 3D sensors have developed rapidly. However, the obtained 3D point cloud is inevitably contaminated with noise due to physical and environmental factors, which has a negative impact on recognition task. To address this problem, a complete object recognition framework for 3D noisy point cloud is presented into which a pre-processing step of filtering is integrated for the first time. In the filtering phase, our two proposed approaches, named Guided 3D Point Cloud Filter (G3DF) and Iterative Guidance Normal Filter (IGNF), are taken into account to produce high-quality point cloud model. Then, on the basis of advantages of local-based and global-based descriptors, a new type of feature descriptor, called Local-to-Global Histogram (LGH), is proposed, which contains Local Viewpoint Feature Histogram (LVFH) and Local Ensemble of Shape Function (LESF). Experimental results show that the comprehensive classification performance yielded by using proposed filters and descriptors is competitive compared to other state-of-the-art combinations. In particularly, the composition of G3DF and LVFH is more suited for real-time applications.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 62002299), and the Natural Science Foundation of Chongqing of China (No. cstc2020jcyj-msxmX0126), and the Fundamental Research Funds for the Central Universities (No. SWU120005)

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Xian-Feng Han

  2. Tianjin University, Tianjin, 300072, China

    Xin-Yu Yan

  3. Chang’an University, Xi’an, ShaanXi, 710064, China

    Shi-Jie Sun

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  1. Xian-Feng Han
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  2. Xin-Yu Yan
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  3. Shi-Jie Sun
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Correspondence to Xian-Feng Han.

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Han, XF., Yan, XY. & Sun, SJ. Novel methods for noisy 3D point cloud based object recognition. Multimed Tools Appl 80, 26121–26143 (2021). https://doi.org/10.1007/s11042-021-10794-3

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