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Object Detection from Large-Scale 3D Datasets using Bottom-up and Top-down Descriptors (2008)
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@MISC{Patterson08objectdetection,
author = {Alexander Patterson and IV and Kostas Daniilidis and et al.},
title = {Object Detection from Large-Scale 3D Datasets using Bottom-up and Top-down Descriptors},
year = {2008}
}
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Abstract
We propose an approach for detecting objects in large-scale range datasets that combines bottom-up and top-down processes. In the bottom-up stage, fast-to-compute local descriptors are used to detect potential target objects. The object hypotheses are verified after alignment in a top-down stage using global descriptors that capture larger scale structure information. We have found that the combination of spin images and Extended Gaussian Images, as local and global descriptors respectively, provides a good trade-off between efficiency and accuracy. We present results on real outdoors scenes containing millions of scanned points and hundreds of targets. Our results compare favorably to the state of the art by being applicable to much larger scenes captured under less controlled conditions, by being able to detect object classes and not specific instances, and by being able to align the query with the best matching model accurately, thus obtaining precise segmentation.
Keyphrases
object detection top-down descriptor global descriptor large-scale range datasets scale structure information real outdoors spin image specific instance extended gaussian image top-down stage object class fast-to-compute local descriptor potential target object top-down process scanned point controlled condition present result object hypothesis matching model bottom-up stage good trade-off precise segmentation
