Given two images, we can estimate the relative camera pose between them by establishing image-to-image correspondences. Usually, correspondences are 2D-to-2D and the pose we estimate is defined only up to scale. Some applications, aiming at instant augmented reality anywhere, require scale-metric pose estimates, and hence, they rely on external depth estimators to recover the scale. We present MicKey, a keypoint matching pipeline that is able to predict metric correspondences in 3D camera space. By learning to match 3D coordinates across images, we are able to infer the metric relative pose without depth measurements. Depth measurements are also not required for training, nor are scene reconstructions or image overlap information. MicKey is supervised only by pairs of images and their relative poses. MicKey achieves state-of-the-art performance on the Map-Free Relocalisation benchmark while requiring less supervision than competing approaches.

给定两幅图像，我们可以通过建立图像到图像的对应关系来估计它们之间的相对相机位姿。通常，这些对应关系是二维到二维的，我们估计的位姿只有比例上的定义。一些旨在实现随时随地的增强现实的应用需要比例度量的位姿估计，因此它们依赖于外部深度估计器来恢复比例。我们提出了MicKey，这是一个能够在三维相机空间中预测度量对应关系的关键点匹配流程。通过学习跨图像匹配三维坐标，我们能够推断出度量上的相对位姿，而不需要深度测量。训练过程中无需深度测量、场景重建或图像重叠信息。MicKey仅通过图像对及其相对位姿进行监督训练。MicKey在无地图重定位基准测试中取得了最先进的性能，并且所需监督较竞争方法更少。

在3D中匹配2D图像：来自度量对应的度量相对位姿