This paper presents a new algorithm to estimate absolute camera pose given an
axis of the camera's rotation matrix. Current algorithms solve the problem via
algebraic solutions on limited input domains. This paper shows that the problem
can be solved efficiently by finding the intersection points of a hyperbola and
the unit circle. The solution can flexibly accommodate combinations of point
and line features in minimal and overconstrained configurations. In addition,
the two special cases of planar and minimal configurations are identified to
yield simpler closed-form solutions. Extensive experiments validate the
approach.

该论文提出了一种估计相机的绝对姿态的新算法，该算法通过找到双曲线和单位圆之间的交点来高效解决问题，可以适应点和线特征的不同配置，并且还提到了平面和最小配置的简化解决方案，并通过大量实验证实了该方法。

PoseGravity: 通过点和线的姿态估计和轴向先验

PoseGravity: Pose Estimation from Points and Lines with Axis Prior

Conventional absolute camera pose via a Perspective-n-Point (PnP) solver
often assumes that the correspondences between 2D image pixels and 3D points
are given. When the correspondences between 2D and 3D points are not known a
priori, the task becomes the much more challenging blind PnP problem. This
paper proposes a deep CNN model which simultaneously solves for both the 6-DoF
absolute camera pose and 2D--3D correspondences. Our model comprises three
neural modules connected in sequence. First, a two-stream PointNet-inspired
network is applied directly to both the 2D image keypoints and the 3D scene
points in order to extract discriminative point-wise features harnessing both
local and contextual information. Second, a global feature matching module is
employed to estimate a matchability matrix among all 2D--3D pairs. Third, the
obtained matchability matrix is fed into a classification module to
disambiguate inlier matches. The entire network is trained end-to-end, followed
by a robust model fitting (P3P-RANSAC) at test time only to recover the 6-DoF
camera pose. Extensive tests on both real and simulated data have shown that
our method substantially outperforms existing approaches, and is capable of
processing thousands of points a second with the state-of-the-art accuracy.

本文提出了一种深度 CNN 模型，该模型能够同时解决 6 自由度绝对摄像机姿态和 2D-3D 对应关系，通过全局特征匹配模块和分类模块实现了对无先验知识的 2D-3D 对应关系的盲目 PnP 问题的有效解决。