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3D Object Recognition from Range Images using Local Feature Histograms

G. Hetzel, B. Leibe, P. Levi, B. Schiele
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001)
Hawaii, USA, December 2001

Abstract

This paper explores a view-based approach to recognize free-form objects in range images. We are using a set of local features that are easy to calculate and robust to partial occlusions. By combining those features in a multidimensional histogram, we can obtain highly discriminant classifiers without the need for segmentation. Recognition is performed using either histogram matching or a probabilistic recognition algorithm. We compare the performance of both methods in the presence of occlusions and test the system on a database of almost 2000 full-sphere views of 30 free-form objects. The system achieves a recognition accuracy above 93% on ideal images, and of 89% with 20% occlusion.


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@InProceedings{eth_biwi_00416,
  author = {G. Hetzel and B. Leibe and P. Levi and B. Schiele},
  title = { 3D Object Recognition from Range Images using Local Feature Histograms},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001)},
  year = {2001},
  month = {December},
  keywords = {}
}