Graspable Parts Recognition in Man-Made 3D Shapes

  • Hamid Laga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7725)


We address the problem of automatic recognition of graspable parts in man-made 3D shapes, which exhibit high intra-class variability that cannot be captured with geometric descriptors alone. We observe that, in the presence of significant geometric and topological variations, the context of a part within a 3D shape provides important cues about its functionality. We propose to model the context as structural relationships between shape parts and use them, in addition to part geometry, as cues for identifying automatically the graspable parts. We design a set of spatial relationships that can be extracted from general shapes. Then, we propose a new similarity measure that captures a part context and enables better recognition of graspable parts. We use this property to design a classifier that learns the semantics of a shape part. We demonstrate that our approach outperforms the state-of-the-art approaches that are purely geometric-based.


Iterative Close Point Geometric Descriptor Shape Part Contextual Relationship Topological Variation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hamid Laga
    • 1
  1. 1.Phenomics and Bioinformatics Research CentreUniversity of South AustraliaAustralia

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