We introduce LightGlue, a deep neural network that learns to match local
features across images. We revisit multiple design decisions of SuperGlue, the
state of the art in sparse matching, and derive simple but effective
improvements. Cumulatively, they make LightGlue more efficient - in terms of
both memory and computation, more accurate, and much easier to train. One key
property is that LightGlue is adaptive to the difficulty of the problem: the
inference is much faster on image pairs that are intuitively easy to match, for
example because of a larger visual overlap or limited appearance change. This
opens up exciting prospects for deploying deep matchers in latency-sensitive
applications like 3D reconstruction. The code and trained models are publicly
available at this https URL

LightGlue 是一种深度神经网络，可以跨图像学习匹配本地特征，并通过多个简单但有效的改进使其更加高效、精确、易于训练，尤其适用于对于视觉重叠度较高或外观变化有限的图像匹配问题，这为 3D 重建等对延迟敏感的应用开启了希望。

LightGlue：轻松速配局部特征

LightGlue: Local Feature Matching at Light Speed

In this paper, we present a decomposition model for stereo matching to solve
the problem of excessive growth in computational cost (time and memory cost) as
the resolution increases. In order to reduce the huge cost of stereo matching
at the original resolution, our model only runs dense matching at a very low
resolution and uses sparse matching at different higher resolutions to recover
the disparity of lost details scale-by-scale. After the decomposition of stereo
matching, our model iteratively fuses the sparse and dense disparity maps from
adjacent scales with an occlusion-aware mask. A refinement network is also
applied to improving the fusion result. Compared with high-performance methods
like PSMNet and GANet, our method achieves $10-100\times$ speed increase while
obtaining comparable disparity estimation results.

本文提出了一种分解模型来解决在分辨率提高时计算成本（时间和内存成本）过高的立体匹配问题，该模型仅在很低的分辨率下运行密集匹配，使用不同的高分辨率下的稀疏匹配来逐步恢复失去细节的视差，并通过一个有遮挡感知掩码迭代地融合相邻尺度上的稀疏和密集视差图，同时应用细化网络来改善融合结果，相比高性能方法如 PSMNet 和 GANet，我们的方法在获得可比较的视差估计结果时提高了 $10 - 100 	imes$ 的速度。

立体匹配分解模型

A Decomposition Model for Stereo Matching

We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet feature-matching using a new loss based on SURF
detector and dense descriptor maps. A network is trained to output synthetic
images optimised for feature matching given only an input RGB image, and these
generated images are used to localize the robot against a previously built map
using traditional sparse matching approaches. We benchmark our results using
multiple traversals of the Oxford RobotCar Dataset over a year-long period,
using one traversal as a map and the other to localise. We show that this
method significantly improves place recognition and localisation under changing
and adverse conditions, while reducing the number of mapping runs needed to
successfully achieve reliable localisation.

本文提出了一种通过学习可逆的生成器将图像从一种条件转换到另一种的方法，以此改善在外观强烈变化的情况下的视觉场所识别和度量本地化效果，并基于 SURF 检测器和密集描述符映射的新损失进行特征匹配。数据测试表明，这种方法能够在减少映射运行次数的同时，在多种环境下显著提高地点识别和定位的效果。