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Gmd: Gaussian mixture descriptor for pair matching of 3D fragments

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Abstract

In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In this paper, we propose a novel local descriptor that uses the Gaussian Mixture Model (GMM) to fit the distribution of points, allowing for the description and matching of fractured surfaces of fragments. Our method involves dividing a local surface patch into concave and convex regions for estimating the k value of GMM. Then the final Gaussian Mixture Descriptor (GMD) of the fractured surface is formed by merging the regional GMDs. To measure the similarities between GMDs for determining adjacent fragments, we employ the \(L_2\) distance and align the fragments using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP). The extensive experiments on real-scanned public datasets and Terracotta datasets demonstrate the effectiveness of our approach; furthermore, the comparisons with several existing methods also validate the advantage of the proposed method.

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Data availability

This study utilized two datasets: the publicly available Geometric Fracture dataset, accessible online at https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/LZNPKB, and a dataset created by the authors. The Geometric Fracture dataset can be accessed online, while the authors’ dataset is not publicly available due to privacy and copyright reasons. Interested individuals can contact the authors for access to the dataset.

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Acknowledgements

The authors would like to thank all the editors and reviewers for their valuable comments and the Institute of Visualization of Northwest University of China for providing the models of Terracotta fragments.

Funding

This work was supported by the Shaanxi Science and Technology Association Youth Talent Support Program[20230115], National Natural Science Foundation of China (NSFC) [61802311].

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Author notes

  1. Meijun Xiong and Zhenguo Shi have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xuefu Road, No. 1, Changan District, Xi’an City, 710127, China

    Meijun Xiong, Zhenguo Shi, Xinyu Zhou, Yuhe Zhang & Shunli Zhang

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  1. Meijun Xiong
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Correspondence to Yuhe Zhang or Shunli Zhang.

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Communicated by Yongdong Zhang.

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Xiong, M., Shi, Z., Zhou, X. et al. Gmd: Gaussian mixture descriptor for pair matching of 3D fragments. Multimedia Systems 30, 326 (2024). https://doi.org/10.1007/s00530-024-01519-1

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