论文标题
P1AC：从单个仿射对应关系重新审视绝对姿势
P1AC: Revisiting Absolute Pose From a Single Affine Correspondence
论文作者
论文摘要
传统上，仿射对应关系用于改善宽基线的特征匹配。虽然最近的工作已成功使用仿射对应关系来解决各种相对摄像头姿势估计问题，但对它们在绝对姿势估计中的使用的关注较少。我们将第一个通用解决方案引入了估计校准相机姿势的问题，只有对定向点和仿射对应关系的单个观察结果。我们方法的优点（P1AC）是，与传统的基于点的方法（P3P）相比，它仅需要单个对应关系，从而在强大的估计中大大降低了组合物。 P1AC提供了一种通用解决方案，该解决方案可以消除先前工作中做出的限制性假设，并且适用于基于图像的大规模本地化。我们提出了对P1AC问题的最小解决方案，并在合成数据上评估了我们的新颖求解器，显示了其在各种类型噪声下的数值稳定性和性能。在基于标准的基于图像的定位基准上，我们显示P1AC比广泛使用的P3P算法获得更准确的结果。我们方法的代码可在https://github.com/jonathanventura/p1ac/上获得。
Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://github.com/jonathanventura/P1AC/ .
