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A data-centric unsupervised 3D mesh segmentation method

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Abstract

In this paper, a novel data-centric approach is proposed for solving the 3D mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each node in a 3D mesh graph. This makes the mesh data more compact and easier to process which is important for reducing computation costs. K-Means clustering is then used to cluster each node according to their node embedding information. This data-centric approach is more computationally efficient than other complex models such as CNN and RNN. The main contribution of this study is the development of a data-centric AI framework that combines node2vec embedding, machine learning, and deep learning techniques. The use of cosine similarity is also adapted to compare and evaluate the trained node embedding vectors with different hyperparameters. Additionally, a new algorithm is developed to determine the optimal cluster number using geodesic distance on the 3D mesh. Overall, this approach provides competitive results compared to existing mesh segmentation methods.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

This work was supported by TUBITAK under the project EEEAG-119E572.

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Authors and Affiliations

  1. Multimedia Informatics Department, METU, Ankara, Turkey

    Talya Tümer Sivri

  2. Computer Engineering Department, METU, Ankara, Turkey

    Yusuf Sahillioğlu

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  1. Talya Tümer Sivri
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Correspondence to Yusuf Sahillioğlu.

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Sivri, T.T., Sahillioğlu, Y. A data-centric unsupervised 3D mesh segmentation method. Vis Comput 40, 2237–2249 (2024). https://doi.org/10.1007/s00371-023-02913-y

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