A novel similarity-covariant feature detector that extracts points whose neighborhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile is presented. The saddle condition is verified efficiently by intensity comparisons on two concentric rings that must have exactly two dark-to-bright and two bright-to-dark transitions satisfying certain geometric constraints. Saddle is a fast approximation of Hessian detector as ORB, that implements the FAST detector, is for Harris detector. We propose to use the matching strategy called the first geometric inconsistent with binary descriptors that is suitable for our feature detector, including experiments with fix point descriptors hand-crafted and learned.

Experiments show that the Saddle features are general, evenly spread and appearing in high density in a range of images. The Saddle detector is among the fastest proposed. In comparison with detector with similar speed, the Saddle features show superior matching performance on number of challenging datasets. Compared to recently proposed deep-learning based interest point detectors and popular hand-crafted keypoint detectors, evaluated for repeatability in the ApolloScape dataset Huang et al. (2018), the Saddle detectors shows the best performance in most of the street-level view sequences a.k.a. traversals.

提出了一种新颖的相似度-协方差特征检测器，该检测器提取的点的邻域在被视为3D强度表面时具有类似鞍形的强度分布。通过对两个同心环进行强度比较，可以有效地验证鞍状条件，两个同心环必须具有两个完全满足某些几何约束的暗到亮和两个亮到暗的过渡。鞍形是Hessian探测器的快速近似值，因为实现FAST探测器的ORB用于Harris探测器。我们建议使用适合于我们的特征检测器的匹配策略（称为第一个与二进制描述符不一致的几何形状），包括使用手工制作和学习的定点描述符进行的实验。

实验表明，鞍形特征是通用的，均匀分布的，并且在一系列图像中以高密度出现。鞍形检测器是最快提出的检测器之一。与具有类似速度的检测器相比，Saddle功能在具有挑战性的数据集数量上显示出卓越的匹配性能。与最近提出的基于深度学习的兴趣点检测器和流行的手工制作的关键点检测器相比，在ApolloScape数据集Huang等人中评估了可重复性。（2018），鞍形探测器在大多数街景视图序列（即遍历）中均表现出最佳性能。