Event
PnP*: A Certifiably Globally Optimal Camera Pose Solver
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Bang-Shien Chen, Caitrin E. Eaton, and David M. Rosen propose PnP*, which they describe as the first practical certifiable Perspective-n-Point solver: it returns globally optimal camera poses while guaranteeing efficient computation. The authors state they have proven that the convex relaxation used by the solver is exact under moderate noise, and that by exploiting chordal sparsity patterns and quotient-ring reduction, they make this relaxation solvable and scalable. Numerical validation shows that PnP* returns provably correct camera poses at rates exceeding 10 Hz. The aforementioned exactness, scalability, and real-time performance are all claims made by the authors in the paper.
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- PnP*: A Certifiable Solver for Perspective-n-Point
The authors propose PnP*, which they describe as the first practical certifiable solver that guarantees efficient computation while returning a globally optimal camera pose. The paper proves that the convex relaxation is exact under moderate noise, and uses chordal sparsity patterns and quotient ring reduction to make the relaxation tractable and scalable. Numerical validation shows that PnP* returns provably correct camera poses at over 10 Hz.
arXiv cs.RO ·