Graspable parts recognition in man-made 3D shapes
Date
2012
Authors
Laga, H.
Editors
Lee, K.M.
Advisors
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Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012 / Lee, K.M. (ed./s), pp.552-564
Statement of Responsibility
Conference Name
11th Asian Conference on Computer Vision (ACCV 2012) (5 Nov 2012 - 9 Nov 2012 : Daejeon, Korea)
Abstract
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.
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Copyright 2013 Springer-Verlag Berlin Heidelberg