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 two parameterizations of the P1AC problem and evaluate our novel solvers on synthetic data showing their 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.
翻译:虽然最近的工作成功地利用了类似信件来解决各种相对照相机引起的估计问题,但较少注意在绝对表面估计中使用这些信件的问题。我们提出了第一个总体解决办法,即根据对定向点和近距对应物的单一观察来估计校准相机的构成问题。我们的方法(P1AC)的优点是,与传统的点基方法(P3P)相比,它只需要一个单一的通信,大大减少了稳健估计的组合计算器。P1AC提供了一种一般性解决办法,消除了先前工作中的限制性假设,并适用于大规模基于图像的本地化。我们提出了P1AC问题的两个参数,并评估了我们在各种噪音下显示其数字稳定性和性能的合成数据的新解析器。关于标准基于图像的本地化基准,我们显示P1AC取得的结果比广泛使用的P3P算法更准确。