3D Shape Matching based on Geodesic Distance Distributions
| dc.contributor.author | Martinek, Michael | en_US |
| dc.contributor.author | Ferstl, Matthias | en_US |
| dc.contributor.author | Grosso, Roberto | en_US |
| dc.contributor.editor | Michael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preim | en_US |
| dc.date.accessioned | 2013-11-08T10:35:42Z | |
| dc.date.available | 2013-11-08T10:35:42Z | |
| dc.date.issued | 2012 | en_US |
| dc.description.abstract | In this work, we present a signature for 3D shapes which is based on the distribution of geodesic distances. Our shape descriptor is invariant with respect to rotation and scaling as well as articulations of the object. It consists of shape histograms which reflect the geodesic distance distribution of randomly chosen pairs of surface points as well as the distribution of geodesic eccentricity and centricity. We show, that a combination of these shape histograms provides good discriminative power to find similar objects in 3D databases even if they are differently articulated. In order to improve the efficiency of the feature extraction, we employ a fast voxelization method and compute the geodesic distances on a boundary voxel representation of the objects. | en_US |
| dc.description.seriesinformation | Vision, Modeling and Visualization | en_US |
| dc.identifier.isbn | 978-3-905673-95-1 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/PE/VMV/VMV12/219-220 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.5.3 [Computer Graphics] | en_US |
| dc.subject | Computational Geometry and Object Modeling | en_US |
| dc.subject | Geometric algorithms | en_US |
| dc.subject | languages | en_US |
| dc.subject | and systems | en_US |
| dc.title | 3D Shape Matching based on Geodesic Distance Distributions | en_US |
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