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Published July 2007 | public
Journal Article

Evaluation of features detectors and descriptors based on 3D objects

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 degrees.

Additional Information

© 2007 Springer Science + Business Media. Received February 3, 2006; Revised July 18, 2006; Accepted July 26, 2006. First online version published in September, 2006.

Additional details

Created:
August 19, 2023
Modified:
October 26, 2023