Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2387-400. doi: 10.1109/TPAMI.2013.36.
We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.
我们提出了一种新的方法，用于从一组 3D 到 2D 点对应中估计相机的姿态和焦距。与竞争方法相比，我们的方法具有优势，因为它不仅比现有的封闭形式解更准确，而且比迭代解更快、更准确。我们的方法受到 EPnP 算法的启发，这是最近针对校准情况的 O(n)解决方案。然而，我们表明，考虑焦距作为附加未知项会使原始方法的线性化和再线性化技术不再有效，尤其是在存在大量噪声的情况下。我们提出了新的方法学来规避这一限制，称为穷举线性化和穷举再线性化，它们以封闭形式对解决方案空间进行系统探索。该方法在真实和合成数据上进行了评估，我们的结果表明，除了产生精确的焦距估计外，所恢复的相机姿态几乎与使用 EPnP 计算的姿态一样准确，EPnP 假设相机已校准。
