Central to the application of many multi-view geometry algorithms is the extraction of matching points between multiple viewpoints, enabling classical tasks such as camera pose estimation and 3D reconstruction. Many approaches that characterize these points have been proposed based on hand-tuned appearance models or data-driven learning methods. We propose Soft Expectation and Deep Maximization (SEDM), an iterative unsupervised learning process that directly optimizes the repeatability of the features by posing the problem in a similar way to expectation maximization (EM). We found convergence to be reliable and the new model to be more lighting invariant and better at localize the underlying 3D points in a scene, improving SfM quality when compared to other state of the art deep learning detectors.

翻译：许多多视图几何算法应用的核心是提取多种观点之间的匹配点,使像相机构成估计和3D重建这样的古典任务得以进行。许多这些点的特点是根据手调外观模型或数据驱动的学习方法提出的。我们提议了软期望和深层最大化(SEDM),这是一个迭代的、不受监督的学习过程,它直接优化了这些特征的重复性,以与预期最大化相似的方式提出问题(EM ) 。我们发现,趋同是可靠的,新的模式是更易变的照明,在现场将基底的3D点本地化方面做得更好,在与其他先进的深层学习探测器相比,SfM质量得到改善。