In this work, we address the problem of refining the geometry of local image
features from multiple views without known scene or camera geometry. Current
approaches to local feature detection are inherently limited in their keypoint
localization accuracy because they only operate on a single view. This
limitation has a negative impact on downstream tasks such as
Structure-from-Motion, where inaccurate keypoints lead to large errors in
triangulation and camera localization. Our proposed method naturally
complements the traditional feature extraction and matching paradigm. We first
estimate local geometric transformations between tentative matches and then
optimize the keypoint locations over multiple views jointly according to a
non-linear least squares formulation. Throughout a variety of experiments, we
show that our method consistently improves the triangulation and camera
localization performance for both hand-crafted and learned local features.

本研究提出了一种基于多视图三角化和非线性最小二乘的方法来优化局部图像特征的定位精度，实验证明该方法能够提高手工制作和学习的局部特征的三角化和相机定位性能。