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

Matching 2D Images in 3D: Metric Relative Pose from Metric  Correspondences

The objective of this work is to infer the 3D shape of an object from a
single image. We use sculptures as our training and test bed, as these have
great variety in shape and appearance.
To achieve this we build on the success of multiple view geometry (MVG) which
is able to accurately provide correspondences between images of 3D objects
under varying viewpoint and illumination conditions, and make the following
contributions: first, we introduce a new loss function that can harness
image-to-image correspondences to provide a supervisory signal to train a deep
network to infer a depth map. The network is trained end-to-end by
differentiating through the camera. Second, we develop a processing pipeline to
automatically generate a large scale multi-view set of correspondences for
training the network. Finally, we demonstrate that we can indeed obtain a depth
map of a novel object from a single image for a variety of sculptures with
varying shape/texture, and that the network generalises at test time to new
domains (e.g. synthetic images).

本文的研究目的是从单个图像中推断出物体的三维形状，为了实现这一目标，本文使用雕塑作为实验数据集，并基于多视角几何（MVG）的成功经验，提出了一种新的损失函数，利用图像与图像之间的对应关系训练深度网络，完成端到端的单张图像深度估计任务。同时，为了自动地生成大规模的多视图对应数据集，本文提出了一种数据处理方法。在多种雕塑的数据集上的实验表明，本方法可以从单张图像中推断出新物体的三维形状，并且在测试时可以泛化到新的领域（如合成图像）中。