  We propose a novel camera pose estimation or perspective-n-point (PnP)
algorithm, based on the idea of consistency regions and half-space
intersections. Our algorithm has linear time-complexity and a squared
reconstruction error that decreases at least quadratically, as the number of
feature point correspondences increase.
  Inspired by ideas from triangulation and frame quantisation theory, we define
consistent reconstruction and then present SHAPE, our proposed consistent pose
estimation algorithm. We compare this algorithm with state-of-the-art pose
estimation techniques in terms of accuracy and error decay rate. The
experimental results verify our hypothesis on the optimal worst-case quadratic
decay and demonstrate its promising performance compared to other approaches.
