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A noval weighted distance and local density double histogram used in 3D local feature description

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Abstract

In the field of computer vision, it is a challenging task to accurately describe local features in point clouds with different noise, resolution and low overlap rate. Weighted distance and local density double histogram (WDLDDH), an effective and robust 3D local feature description method, is proposed in this paper. There are two key ideas in the method. Firstly, secondary feature matching is introduced in the matching process to enhance matching accuracy. Secondly, two novel feature descriptors are proposed. One is weighted distance descriptor, the other is local sphere density descriptor. The former processes the distance between points in the local point cloud and the spatial marked points by using the method of “reciprocal weighted smoothing,” which makes the feature descriptor a high level of robustness. The latter adds an effective weight and increases the matching dimensions to correct the incorrect point pairs. The effectiveness of WDLDDH is verified using publicly available datasets, including the Stanford Repository and the SHOT project page. WDLDDH performs better in these commonly used accuracy evaluation metrics, such as Recall versus 1-Precision Curve (RPC), AUC values, and MSE. The experiments fully demonstrate that WDLDDH possesses superior stability and robustness.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Database availability statement

For detailed information about the public database we use, please visit the [http://graphics.stanford.edu/data/3Dscanrep] [www.vision.deis.unibo.it/SHOT]

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Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant No 51775237), Key R&D Projects of the Ministry of Science and Technology of China (Grant Nos. 2017YFA0701200 and 2018YFB1107600), Key R&D Projects of Jilin province of China (Grant Nos 20200401121GX and 20200401144GX), Key scientific research project of Jilin Provincial Department of Education (Grant No JJKH20200972KJ) and Graduate Innovation Funds of Jilin University (Grant Nos. 419100201114 and 101832020CX122).

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China

    Xiaoya Li, Shijun Ji & Ji Zhao

  2. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, China

    Xiaoya Li, Shijun Ji & Ji Zhao

  3. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

    Ji Zhao

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  1. Xiaoya Li
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Correspondence to Shijun Ji.

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Li, X., Ji, S. & Zhao, J. A noval weighted distance and local density double histogram used in 3D local feature description. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19141-8

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