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 https://github.com/cvg/LightGlue.

翻译：本文提出LightGlue——一种用于学习跨图像局部特征匹配的深度神经网络。我们重新审视了稀疏匹配领域当前最优方法SuperGlue的多项设计决策，并推导出简单但有效的改进方案。这些改进的综合效果使LightGlue在内存与计算效率、匹配精度及训练便捷性方面均得到提升。其关键特性在于能够自适应问题难度：对于直观上易于匹配的图像对（例如因视觉重叠区域较大或外观变化有限），推理速度显著加快。这一特性为在三维重建等延迟敏感型应用中部署深度匹配器开辟了令人兴奋的前景。相关代码与预训练模型已开源在https://github.com/cvg/LightGlue。