A data-centric unsupervised 3D mesh segmentation method

Sivri T. T., SAHİLLİOĞLU Y.

Visual Computer, cilt.40, sa.4, ss.2237-2249, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 4
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00371-023-02913-y
  • Dergi Adı: Visual Computer
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.2237-2249
  • Anahtar Kelimeler: 3D mesh segmentation, Embedding, Geodesic distance, K-Means, Node2vec, Unsupervised learning
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.