3D Object Recognition from Range Images using Local Feature Histograms

G√ľnther Hetzel, Bastian Leibe, Paul Levi, Bernt Schiele
Conference on Computer Vision and Pattern Recognition (CVPR'01)

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.

» Show BibTeX

title={3D Object Recognition from Range Images using Local Feature Histograms}},
author={{Hetzel, G{\"u}nter and Leibe, Bastian and Levi, Paul and Schiele, Bernt}},
booktitle={{Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on}},

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