ML Anthology

3D Object Recognition from Range Images Using Local Feature Histograms

Günter Hetzel, Bastian Leibe, Paul Levi, Bernt Schiele
CVPR 2001 pp. II:394-399
doi:10.1109/CVPR.2001.990988 /cvpr/2001/hetzel2001cvpr-d/

Abstract

The 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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Cite

Text

Hetzel et al. "3D Object Recognition from Range Images Using Local Feature Histograms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990988

Markdown

[Hetzel et al. "3D Object Recognition from Range Images Using Local Feature Histograms." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/hetzel2001cvpr-d/) doi:10.1109/CVPR.2001.990988

BibTeX

@inproceedings{hetzel2001cvpr-d,
  title     = {{3D Object Recognition from Range Images Using Local Feature Histograms}},
  author    = {Hetzel, Günter and Leibe, Bastian and Levi, Paul and Schiele, Bernt},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {II:394-399},
  doi       = {10.1109/CVPR.2001.990988},
  url       = {https://mlanthology.org/cvpr/2001/hetzel2001cvpr-d/}
}