论文标题
学习指导当地功能匹配
Learning to Guide Local Feature Matches
论文作者
论文摘要
我们解决了图像之间找到准确且可靠的关键点对应关系的问题。我们提出了一种基于学习的方法，以通过学习的近似图像匹配来指导本地功能匹配。我们的方法可以将SIFT的结果提升到类似于最先进的深层描述符的水平，例如SuperPoint，ContextDesc或D2-Net，并可以提高这些描述符的性能。我们介绍和研究不同水平的监督以学习粗略的对应关系。尤其是，我们表明，来自两极几何形状的弱监督导致的性能高于更强但更偏见的点水平监督，并且对弱图像级别的监督有了明显的改善。我们通过评估我们在YFCC100M数据集上本地化互联网图像的定位和在THESUN3D数据集上的室内图像的定位，以在各种条件下证明我们的方法的好处，以在Aachen Day-night Benchmark上进行稳健定位，并在使用LTLL历史图像数据的有挑战性的条件下进行3D构造。
We tackle the problem of finding accurate and robust keypoint correspondences between images. We propose a learning-based approach to guide local feature matches via a learned approximate image matching. Our approach can boost the results of SIFT to a level similar to state-of-the-art deep descriptors, such as Superpoint, ContextDesc, or D2-Net and can improve performance for these descriptors. We introduce and study different levels of supervision to learn coarse correspondences. In particular, we show that weak supervision from epipolar geometry leads to performances higher than the stronger but more biased point level supervision and is a clear improvement over weak image level supervision. We demonstrate the benefits of our approach in a variety of conditions by evaluating our guided keypoint correspondences for localization of internet images on the YFCC100M dataset and indoor images on theSUN3D dataset, for robust localization on the Aachen day-night benchmark and for 3D reconstruction in challenging conditions using the LTLL historical image data.
