We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. The direction can come from an Inertial Measurement Unit that is a standard component of recent consumer devices, e.g., smartphones. We provide an exhaustive analysis of minimal line configurations and derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. Additionally, we design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization. Combining all solvers in a hybrid robust estimator, our method achieves increased accuracy even with a rough prior. Experiments on synthetic and real-world datasets demonstrate the superior accuracy of our method compared to the state of the art, while having comparable runtimes. We further demonstrate the applicability of our solvers for relative rotation estimation. The code is available at https://github.com/cvg/VP-Estimation-with-Prior-Gravity.

翻译：本文研究如何利用先验垂直方向估计曼哈顿框架（即三个正交消失点）及相机未知焦距的问题。该垂直方向可来自惯性测量单元（IMU），该单元已成为智能手机等现代消费电子设备的标准组件。我们全面分析了最小直线配置，推导出两种新型2线求解器，其中一种可避免现有求解器的奇异性缺陷。此外，我们设计了一种基于任意数量直线的新型非最小方法，用于提升局部优化性能。通过将所有求解器集成到混合鲁棒估计器中，即使先验信息粗糙，本方法仍能实现更高精度。在合成数据集与真实世界数据集上的实验表明，本方法在保持可比运行时间的同时，相比现有技术水平具有更优的精度。进一步地，我们验证了所提求解器在相对旋转估计中的适用性。代码开源于 https://github.com/cvg/VP-Estimation-with-Prior-Gravity。