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Evaluation of features detectors and descriptors based on 3D objects

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2 Author(s)
Moreels, P. ; California Inst. of Technol., Pasadena, CA, USA ; Perona, P.

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. We collect 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 changes and scale changes. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25-30°.

Published in:

Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on  (Volume:1 )

Date of Conference:

17-21 Oct. 2005