Automatic Generator of Minimal Problem Solvers

  • Authors:

  • Zuzana Kukelova;Martin Bujnak;Tomas Pajdla

  • Affiliations:

  • Center for Machine Perception, Czech Technical University, Prague;Center for Machine Perception, Czech Technical University, Prague and Microsoft Corporation, ;Center for Machine Perception, Czech Technical University, Prague

  • Venue:
  • ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
  • Year:
  • 2008

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Abstract

Finding solutions to minimal problems for estimating epipolar geometry and camera motion leads to solving systems of algebraic equations. Often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. The state of the art approach for constructing such algorithms is the Gröbner basis method for solving systems of polynomial equations. Previously, the Gröbner basis solvers were designed ad hoc for concrete problems and they could not be easily applied to new problems. In this paper we propose an automatic procedure for generating Gröbner basis solvers which could be used even by non-experts to solve technical problems. The input to our solver generator is a system of polynomial equations with a finite number of solutions. The output of our solver generator is the Matlab or C code which computes solutions to this system for concrete coefficients. Generating solvers automatically opens possibilities to solve more complicated problems which could not be handled manually or solving existing problems in a better and more efficient way. We demonstrate that our automatic generator constructs efficient and numerically stable solvers which are comparable or outperform known manually constructed solvers. The automatic generator is available at http://cmp.felk.cvut.cz/minimal