3D Object Recognition from Range Images using Local Feature Histograms

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.

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Bastian Leibe, Bernt Schiele, Günter Hetzel, Paul Levi
• Keywords: Computer Vision, CVPR 2001, Free-form Objects, Histogram Matching, Multidimensional Histogram, Partial Occlusions, Probabilistic Recognition Algorithm