We present a novel method for local image feature matching. Instead of
performing image feature detection, description, and matching sequentially, we
propose to first establish pixel-wise dense matches at a coarse level and later
refine the good matches at a fine level. In contrast to dense methods that use
a cost volume to search correspondences, we use self and cross attention layers
in Transformer to obtain feature descriptors that are conditioned on both
images. The global receptive field provided by Transformer enables our method
to produce dense matches in low-texture areas, where feature detectors usually
struggle to produce repeatable interest points. The experiments on indoor and
outdoor datasets show that LoFTR outperforms state-of-the-art methods by a
large margin. LoFTR also ranks first on two public benchmarks of visual
localization among the published methods.

本文提出了一种新的局部图像特征匹配方法，使用 Transformer 的自注意层和交叉注意层得出特征描述符，在低纹理区域中实现稠密匹配，在室内和室外数据集上表现优异，成为视觉定位的新的 state-of-the-art 方法。

LoFTR: 基于 Transformer 的无检测器局部特征匹配

LoFTR: Detector-Free Local Feature Matching with Transformers

Initializing optical flow field by either sparse descriptor matching or dense
patch matches has been proved to be particularly useful for capturing large
displacements. In this paper, we present a pyramidal gradient matching approach
that can provide dense matches for highly accurate and efficient optical flow
estimation. A novel contribution of our method is that image gradient is used
to describe image patches and proved to be able to produce robust matching.
Therefore, our method is more efficient than methods that adopt special
features (like SIFT) or patch distance metric. Moreover, we find that image
gradient is scalable for optical flow estimation, which means we can use
different levels of gradient feature (for example, full gradients or only
direction information of gradients) to obtain different complexity without
dramatic changes in accuracy. Another contribution is that we uncover the
secrets of limited PatchMatch through a thorough analysis and design a
pyramidal matching framework based these secrets. Our pyramidal matching
framework is aimed at robust gradient matching and effective to grow inliers
and reject outliers. In this framework, we present some special enhancements
for outlier filtering in gradient matching. By initializing EpicFlow with our
matches, experimental results show that our method is efficient and robust
(ranking 1st on both clean pass and final pass of MPI Sintel dataset among
published methods).

本文提出了一种金字塔渐变匹配方法，可为高精度和高效的光流估计提供密集匹配，并在强健匹配和离群值过滤方面进行了特殊增强，通过在 MPI Sintel 数据集上的表现显示出高效和强健。