SHAPE: Linear-Time Camera Pose Estimation With Quadratic Error-Decay
This addresses the problem of efficient and accurate camera localization for computer vision applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles camera pose estimation by introducing SHAPE, a linear-time algorithm based on consistency regions and half-space intersections, achieving a quadratic decay in reconstruction error as feature correspondences increase, with experimental results confirming optimal worst-case error decay and competitive performance against state-of-the-art methods.
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.