ScPnP: A non-iterative scale compensation solution for PnP problems

摘要

This paper presents an accurate non-iterative method for the Perspective-n-Point problem(PnP). Our main idea is to mitigate scale bias by multiplying an independent inverse average depth variable onto the object space error. The introduced variable is of order 2 in the objective function and the optimality conditions constitute a polynomial system with three third-order and one first-order unknowns. Subsequently, we employ the Dixon resultant method to compute explicit expressions of the action matrix, the eigenvalue decomposition of which determines all the roots of the problem. We further extend this scale compensation technology to sphere cameras and contribute a uniform solver to PnP problems for both camera types. Experimental results confirm that our method is reliable and more accurate than state-of-the-art PnP algorithms.