Albl Cenek, Kukelova Zuzana, Larsson Viktor, Pajdla Tomas
IEEE Trans Pattern Anal Mach Intell. 2020 Jun;42(6):1439-1452. doi: 10.1109/TPAMI.2019.2894395. Epub 2019 Jan 22.
We present minimal, non-iterative solutions to the absolute pose problem for images from rolling shutter cameras. The absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We discuss several camera motion models and propose two feasible rolling shutter camera models for a polynomial solver. In previous work a linearized camera model was used that required an initial estimate of the camera orientation. We show how to simplify the system of equations and make this solver faster. Furthermore, we present a first solution of the non-linearized camera orientation model using the Cayley parameterization. The new solver does not require any initial camera orientation estimate and therefore serves as a standalone solution to the rolling shutter camera pose problem from six 2D-to-3D correspondences. We show that our algorithms outperform P3P followed by a non-linear refinement using a rolling shutter model.
我们针对滚动快门相机图像的绝对位姿问题，提出了最小化、非迭代的解决方案。绝对位姿问题是计算机视觉中的一个关键问题，并且滚动快门存在于当今绝大多数的数码相机中。我们讨论了几种相机运动模型，并为多项式求解器提出了两种可行的滚动快门相机模型。在先前的工作中，使用了一种线性化相机模型，该模型需要相机方向的初始估计值。我们展示了如何简化方程组并使该求解器更快。此外，我们使用凯莱参数化提出了非线性相机方向模型的首个解决方案。新的求解器不需要任何相机方向的初始估计值，因此可作为从六个二维到三维对应关系解决滚动快门相机位姿问题的独立解决方案。我们表明，我们的算法优于P3P，随后使用滚动快门模型进行非线性优化。
