Cube of space sampling for 3D model retrieval

Zong Yao Chen, Chih Fong Tsai, Wei Chao Lin

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)


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.

期刊Applied Sciences (Switzerland)
出版狀態已出版 - 1 12月 2021


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