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VOID: 3D object recognition based on voxelization in invariant distance space

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Abstract

Recognizing 3D objects based on local feature descriptors, in point cloud scenes with occlusion and clutter, is a very challenging task. Most existing 3D local feature descriptors rely on normal information to encode local features, however, they ignore the normal-sign-ambiguity issue, which greatly limits their descriptiveness and robustness. This paper proposes a method called VOxelization in Invariant Distance space for 3D object recognition. First, we propose a VOID descriptor that is invariant to normal-sign-ambiguity, and is also rotation-invariant, distinctive, robust, and efficient. Second, a VOID-based 3D object recognition method considering the self-similarity between local features is proposed to enhance the recognition performance. Five standard datasets are employed to validate our proposed method as well as comparison with the state-of-the-arts. The results suggest that: (1) VOID descriptor is invariant to normal-sign-ambiguity, distinctive, and robust; (2) VOID-based 3D object recognition achieves outstanding recognition performance, i.e., 99.47%, 93.07% and 99.18%, on the U3OR, Queen’s and Ca’ Foscari Venezia datasets, respectively.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 62002295 and 62006025), the Ningbo Natural Science Foundation (No. 202003N4058), the China Postdoctoral Science Foundation (No. 2020M673319), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2021JQ-290 and 2020JQ-210), the State Key Laboratory of Rail Transit Engineering Informatization (FSDI) [Contract No. SKLKZ21-02], and the Fundamental Research Funds for the Central Universities (No. 3102019QD1002).

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Authors and Affiliations

  1. Ningbo Institute of Northwestern Polytechnical University, 218 Qingyi Road, Ningbo, 315103, China

    Jiaqi Yang, Shichao Fan & Zhiqiang Huang

  2. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Jiaqi Yang, Shichao Fan, Zhiqiang Huang & Yanning Zhang

  3. The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an, 710129, China

    Jiaqi Yang, Shichao Fan, Zhiqiang Huang & Yanning Zhang

  4. School of Electronics and Control, Chang’an University, Xi’an, 710064, China

    Siwen Quan

  5. State Key Laboratory of Rail Transit Engineering Informatization (FSDI), Xi’an, 710043, China

    Wei Wang

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Yang, J., Fan, S., Huang, Z. et al. VOID: 3D object recognition based on voxelization in invariant distance space. Vis Comput 39, 3073–3089 (2023). https://doi.org/10.1007/s00371-022-02514-1

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