Camera-pose estimation via projective newton optimization on the manifold

Michel Sarkis, Klaus Diepold

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Determining the pose of a moving camera is an important task in computer vision. In this paper, we derive a projective Newton algorithm on the manifold to refine the pose estimate of a camera. The main idea is to benefit from the fact that the 3-D rigid motion is described by the special Euclidean group, which is a Riemannian manifold. The latter is equipped with a tangent space defined by the corresponding Lie algebra. This enables us to compute the optimization direction, i.e., the gradient and the Hessian, at each iteration of the projective Newton scheme on the tangent space of the manifold. Then, the motion is updated by projecting back the variables on the manifold itself. We also derive another version of the algorithm that employs homeomorphic parameterization to the special Euclidean group. We test the algorithm on several simulated and real image data sets. Compared with the standard Newton minimization scheme, we are now able to obtain the full numerical formula of the Hessian with a 60% decrease in computational complexity. Compared with Levenberg-Marquardt, the results obtained are more accurate while having a rather similar complexity.

Original languageEnglish
Article number6094213
Pages (from-to)1729-1741
Number of pages13
JournalIEEE Transactions on Image Processing
Volume21
Issue number4
DOIs
StatePublished - Apr 2012

Keywords

  • Differential geometry
  • Newton method
  • Riemannian manifold
  • pose estimation

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