P1AC：重新审视基于单一仿射对应的绝对位姿估计
P1AC: Revisiting Absolute Pose From a Single Affine Correspondence
AI总结：
本文提出P1AC，首个仅利用单一仿射对应和有向点观测估计已标定相机绝对位姿的通用最小解，相比P3P显著降低组合复杂度，并在定位基准上取得更高精度。
摘要：
仿射对应传统上被用于改善宽基线条件下的特征匹配。尽管近期工作已成功利用仿射对应解决多种相对相机位姿估计问题，但对其在绝对位姿估计中的应用关注较少。我们提出了首个通用解决方案，用于在给定一个有向点观测和一个仿射对应的情况下估计已标定相机的位姿。与传统的基于点的方法（P3P）相比，我们的方法（P1AC）的优势在于仅需一个对应，从而显著降低了鲁棒估计中的组合复杂度。P1AC提供了一种通用解决方案，消除了先前工作中的限制性假设，适用于大规模基于图像的定位。我们提出了P1AC问题的最小解，并在合成数据上评估了我们的新型求解器，展示了其数值稳定性及在各类噪声下的性能。在标准基于图像的定位基准上，我们表明P1AC比广泛使用的P3P算法取得了更准确的结果。我们的方法代码可在 https://github.com/jonathanventura/P1AC/ 获取。
英文摘要：
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/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
