Evaluation of Features Detectors and Descriptors Based on 3D Objects

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 Hessianaffine 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 .

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Pierre Moreels, Pietro Perona
• Keywords: Computer Vision, ICCV 2005, Popular Feature Detectors, Shape Context Descriptors, SIFT Features, Viewpoint Changes