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2004
IEEE

Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation

12 years 3 months ago
Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation
We propose a novel approach to point matching under large viewpoint and illumination changes that is suitable for accurate object pose estimation at a much lower computational cost than state-of-the-art methods. Most of these methods rely either on using ad hoc local descriptors or on estimating local affine deformations. By contrast, we treat wide baseline matching of keypoints as a classification problem, in which each class corresponds to the set of all possible views of such a point. Given one or more images of a target object, we train the system by synthesizing a large number of views of individual keypoints and by using statistical classification tools to produce a compact description of this view set. At run-time, we rely on this description to decide to which class, if any, an observed feature belongs. This formulation allows us to use a classification method to reduce matching error rates, and to move some of the computational burden from matching to training, which can be p...
Vincent Lepetit, Julien Pilet, Pascal Fua
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Vincent Lepetit, Julien Pilet, Pascal Fua
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