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Orthogonal integral transform for 3D shape recognition with few examples

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Abstract

3D shape recognition with few examples is crucial for applications involving 3D scenes, but typical methods based on surface and view suffer the failure to describe the interior and exterior features uniformly. Thus, we propose 3D orthogonal integral transform (OIT). OIT is composed of three individual integrals over a group of three orthogonal planes rotating to cover all orientations by which the volumetric shape is bisected in integrals. OIT offers the following advantages: (1) It describes a 3D shape structurally from interior to exterior uniformly, which brings about discriminative shape characteristics; and (2) the shape descriptor built on OIT is invariant with respect to translation, scaling and rotation. Furthermore, a fine-grained 3D model dataset (FGModele40) is built on ModelNet40. Experiments show that OIT can provide both discriminative and robust descriptors for 3D shape recognition with few examples. Our proposed OIT outperforms typical state-of-the-art benchmarks evaluated by the protein shape retrieval contest; additionally, it also surpasses other typical deep learning models with respect to the task of 3D shape recognition with few examples on FGModele40.

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Data availability

The data that support the findings of this study has been available at https://github.com/lcd21/OIT/tree/master/FGModele40. More details may be available upon reasonable request by contacting with the corresponding author.

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Acknowledgements

This work was in part supported by NSFC (Grant No. 62176194, Grant No.62101393), the Major project of IoV (Grant No. 2020AAA001), Sanya Science and Education Innovation Park of Wuhan University of Technology (Grant No. 2021KF0031), CSTC (Grant No. cstc2021jcyj-msxmX1148) and the Open Project of Wuhan University of Technology Chongqing Research Institute (ZL2021-6).

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Authors and Affiliations

  1. School of Artificial Intelligence, Guilin University Of Electronic Technology, Guilin, 541004, China

    Chengde Lin

  2. School of Mathematics and Computational Sciences, Wuyi University, Jiangmen, 529020, China

    Peng Wang

  3. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Shengwu Xiong & Ruyi Chen

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  1. Chengde Lin
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  2. Peng Wang
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Contributions

CL: Conceptualization, Methodology, Software, Writing-Original Draft, Formal analysis, Writing-Review & Editing. PW: Validation, Resources, Writing-Review & Editing. SX: Conceptualization, Methodology, Writing-Review & Editing, Project administration. RC: Validation, Resources, Writing-Review & Editing.

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Correspondence to Chengde Lin.

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Lin, C., Wang, P., Xiong, S. et al. Orthogonal integral transform for 3D shape recognition with few examples. Vis Comput 40, 3271–3284 (2024). https://doi.org/10.1007/s00371-023-03030-6

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