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Using Multi-view Recognition to Guide a Robot

A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, L. Van Gool
International Journar of Robotics Research
Vol. 28, No. 8, pp. 976-998, August 2009

Abstract

In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. The method can recognize objects from arbitrary viewpoints and generalizes to instances that have never been observed during training, even if they are partially occluded and appear against cluttered backgrounds. Our approach builds on the Implicit Shape Model of Leibe et al. We extend it to couple recognition to the provision of meta-data useful for a task and to the case of multiple viewpoints by integrating it with the dense multi-view correspondence finder of Ferrari et al. Metadata can be part labels but also depth estimates, information on material types, or any other pixelwise annotation. We present experimental results on wheelchairs, cars, and motorbikes.


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@Article{eth_biwi_00665,
  author = {A. Thomas and V. Ferrari and B. Leibe and T. Tuytelaars and L. Van Gool},
  title = {Using Multi-view Recognition to Guide a Robot},
  journal = {International Journar of Robotics Research},
  year = {2009},
  month = {August},
  pages = {976-998},
  volume = {28},
  number = {8},
  keywords = {object class detection; depth estimation; part decomposition; meta-data transfer}
}