Using spin images for efficient object recognition in cluttered 3D scenes

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Using spin images for efficient object recognition in cluttered 3D scenes (1999)

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by Andrew E. Johnson , Martial Hebert

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	582 - 9 self

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@ARTICLE{Johnson99usingspin,
    author = {Andrew E. Johnson and Martial Hebert},
    title = {Using spin images for efficient object recognition in cluttered 3D scenes},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {1999},
    pages = {433--449}
}

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Abstract

We present a 3-D 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. This research was performed at Carnegie Mellon University and was supported by the US Department Surface matching is a technique from 3-D computer vision that has many applications in the area of robotics and automation. Through surface matching, an object can be recognized in a scene by comparing a sensed surface to an object surface stored in memory. When the object surface is matched to the scene surface, an association is made between something known (the object) and

Keyphrases

spin image efficient object recognition simultaneous recognition object surface multiple object surface matching data level shape descriptor surface mesh sensed surface 3-d computer vision many application robust performance spin-image representation carnegie mellon university u department surface matching compression scheme recognition trial efficient multiple object recognition scene surface 3-d shape-based object recognition system

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