论文标题
通过强大的可区分几何优化，解决盲目的视角N点问题
Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization
论文作者
论文摘要
盲目的透视n点（PNP）是估计相对于场景（给定的2D图像点和3D场景点）估算相机位置和方向的问题，而没有2d-3d的相互了解。由于搜索空间非常大，因此解决姿势和对应的解决方案非常具有挑战性。幸运的是，这是一个耦合的问题：可以很容易地找到姿势，反之亦然。现有方法假设提供了嘈杂的对应关系，可以提供良好的姿势，或者问题大小很小。相反，我们提出了第一个完全端到端的可训练网络，用于有效地解决盲目的PNP问题，即无需姿势先验。我们利用了区分优化问题的最新结果，将拟合的几何模型纳入端到端的学习框架，包括Sinkhorn，RANSAC和PNP算法。我们提出的方法显着优于合成和真实数据的其他方法。
Blind Perspective-n-Point (PnP) is the problem of estimating the position and orientation of a camera relative to a scene, given 2D image points and 3D scene points, without prior knowledge of the 2D-3D correspondences. Solving for pose and correspondences simultaneously is extremely challenging since the search space is very large. Fortunately it is a coupled problem: the pose can be found easily given the correspondences and vice versa. Existing approaches assume that noisy correspondences are provided, that a good pose prior is available, or that the problem size is small. We instead propose the first fully end-to-end trainable network for solving the blind PnP problem efficiently and globally, that is, without the need for pose priors. We make use of recent results in differentiating optimization problems to incorporate geometric model fitting into an end-to-end learning framework, including Sinkhorn, RANSAC and PnP algorithms. Our proposed approach significantly outperforms other methods on synthetic and real data.
