资源论文Automatic Generator of Minimal Problem Solvers

Automatic Generator of Minimal Problem Solvers

2020-03-30 | |  64 |   57 |   0

Abstract

Finding solutions to minimal problems for estimating epipolar geom- etry 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 Gr¨obner basis method for solving systems of polynomial equations. Previously, the Gr¨obner 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 gener- ating Gr ?bner 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 compara- ble or outperform known manually constructed solvers. The automatic generator is available at http://cmp.felk.cvut.cz/minimal 1 .

上一篇:A Linear Time Histogram Metric for Improved SIFT Matching

下一篇:Optimization of Symmetric Transfer Error for Sub-frame Video Synchronization

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...