Nonlinear Mean Shift for Robust Pose Estimation

Raghav Subbarao(1,2), Yakup Genc(2) and Peter Meer(1)

(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA

(2)Real Time Vision and Modeling Group
Siemens Corporate Research
Princeton, NJ

We propose a new robust estimator for camera pose estimation based on a recently developed nonlinear mean shift algorithm. This allows us to treat pose estimation as a clustering problem in the presence of outliers. We compare our method to RANSAC, which is the standard robust estimator for computer vision problems. We also show that under fairly general assumptions our method is provably better than RANSAC. Synthetic and real examples to support our claims are provided.

8th IEEE Workshop on Applications of Computer Vision, Austin, TX, February 2007.
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