Five-Point Motion Estimation Made Easy

Estimating relative camera motion from two views is a classical problem in computer vision. The minimal case for such problem is the so-called five-point-problem, for which the state-of-the-art solution is Nist?er's algorithm [9]. However, due to the heuristic and ad hoc nature of the procedures it applies, to implement it is not so easy for non-expert users. This paper provides a much easier algorithm based on hidden variable resultant technique. Instead of eliminating the unknown variables one by one (i.e., sequentially) using the Gaussian method as in [9], our algorithm eliminates many unknowns all at once. Moreover, in the equation solving stage, instead of back-substituting and solve all the unknowns sequentially, we compute the minimal singular vector of the coefficient matrix, by which all the unknown parameters can be estimated simultaneously. Experiments on both simulation and real images have validated the new algorithm.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Hongdong Li, Richard I. Hartley
• Keywords: Ad Hoc Nature, Computer Vision, Easier Algorithm, ICPR 2006, Minimal Singular Vector