Multi-scale tensor voting for feature extraction from unstructured point clouds

@article{Park2012MultiscaleTV,
  title={Multi-scale tensor voting for feature extraction from unstructured point clouds},
  author={Min Ki Park and Seung Joo Lee and Kwan H. Lee},
  journal={Graphical Models},
  year={2012},
  volume={74},
  pages={197-208}
}
1524-0703/$ see front matter 2012 Elsevier Inc http://dx.doi.org/10.1016/j.gmod.2012.04.008 ⇑ Corresponding author. E-mail addresses: minkp@gist.ac.kr (M.K. Park), s Lee), khlee@gist.ac.kr (K.H. Lee). Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise… CONTINUE READING
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