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Evaluation of Features Detectors and Descriptors based on 3D Objects

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Abstract

We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25–30∘.

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Authors and Affiliations

  1. California Institute of Technology, Pasadena, CA, 91125, USA

    Pierre Moreels & Pietro Perona

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  1. Pierre Moreels
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  2. Pietro Perona
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Correspondence to Pierre Moreels.

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Moreels, P., Perona, P. Evaluation of Features Detectors and Descriptors based on 3D Objects. Int J Comput Vision 73, 263–284 (2007). https://doi.org/10.1007/s11263-006-9967-1

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  • DOI: https://doi.org/10.1007/s11263-006-9967-1

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