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.

中文翻译：

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卷帘快门相机绝对姿势


我们针对卷帘快门相机图像的绝对姿态问题提出了最小的非迭代解决方案。绝对姿势问题是计算机视觉中的一个关键问题，而当今绝大多数数码相机中都存在卷帘快门。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卷帘快门相机模型。在之前的工作中，使用了线性化相机模型，需要对相机方向进行初始估计。我们展示了如何简化方程组并使该求解器更快。此外，我们提出了使用凯莱参数化的非线性相机方向模型的第一个解决方案。新的求解器不需要任何初始相机方向估计，因此可以作为来自六个 2D 到 3D 对应关系的卷帘快门相机姿势问题的独立解决方案。我们证明我们的算法优于 P3P，然后使用卷帘快门模型进行非线性细化。