Abstract
We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.
Keywords
ClutterArtificial intelligenceCognitive neuroscience of visual object recognitionComputer visionComputer science3D single-object recognitionPattern recognition (psychology)Polygon meshObject (grammar)Matching (statistics)Representation (politics)MathematicsComputer graphics (images)Radar
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 21
- Issue
- 5
- Pages
- 433-449
- Citations
- 2604
- Access
- Closed
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Cite This
Andrew Johnson,
Martial Hebert
(1999).
Using spin images for efficient object recognition in cluttered 3D scenes.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 21
(5)
, 433-449.
https://doi.org/10.1109/34.765655
Identifiers
- DOI
- 10.1109/34.765655