Abstract
We propose a novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial effect of enhancing the spatial descriptiveness of the feature and its ability to recognize objects by means of a simple nearest neighbor classifier computed on the descriptor space. Moreover, the definition of repeatable directions can be deployed to efficiently retrieve the 6DOF pose of the objects in a scene. We experimentally demonstrate the effectiveness of the proposed method on a dataset including 23 scenes acquired with the Microsoft Kinect sensor and 25 full-3D models by comparing the proposed approach with state-of-the-art global descriptors. A substantial improvement is presented regarding accuracy in recognition and 6DOF pose estimation, as well as in terms of computational performance.
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© 2012 Springer-Verlag Berlin Heidelberg
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Aldoma, A., Tombari, F., Rusu, R.B., Vincze, M. (2012). OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_12
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DOI: https://doi.org/10.1007/978-3-642-32717-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32716-2
Online ISBN: 978-3-642-32717-9
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