论文标题
Goodpoint：无监督的关键点检测和描述
GoodPoint: unsupervised learning of keypoint detection and description
论文作者
论文摘要
本文介绍了一种新的算法，用于无监督的关键点检测器和描述符，该算法在不同数据集中展示了快速收敛和良好的性能。训练程序使用图像的平均转换。提出的模型学会了检测点并在成对的转换图像上生成描述符，这对于它很容易区分和反复检测。训练有素的模型遵循SuperPoint体系结构，以易于比较，并在HPATCHES数据集的自然图像上展示了类似的性能，并且来自Furdus Image Imagion Registration DataSet的视网膜图像的性能更好，该数据集包含较少的角式特征。对于HPATCHES和其他数据集，还计算了覆盖范围，以更好地估计模型质量。
This paper introduces a new algorithm for unsupervised learning of keypoint detectors and descriptors, which demonstrates fast convergence and good performance across different datasets. The training procedure uses homographic transformation of images. The proposed model learns to detect points and generate descriptors on pairs of transformed images, which are easy for it to distinguish and repeatedly detect. The trained model follows SuperPoint architecture for ease of comparison, and demonstrates similar performance on natural images from HPatches dataset, and better performance on retina images from Fundus Image Registration Dataset, which contain low number of corner-like features. For HPatches and other datasets, coverage was also computed to provide better estimation of model quality.
