Cube of space sampling for 3D model retrieval

Zong Yao Chen, Chih Fong Tsai, Wei Chao Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations


Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method.

Original languageEnglish
Article number11142
JournalApplied Sciences (Switzerland)
Issue number23
StatePublished - 01 12 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • 3D model
  • 3D object
  • Collision detection
  • Content-based retrieval
  • Re-sampling
  • Similarity match


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