Cheng Wentao, Chen Kan, Lin Weisi, Goesele Michael, Zhang Xinfeng, Zhang Yabin
IEEE Trans Image Process. 2019 Oct;28(10):4857-4869. doi: 10.1109/TIP.2019.2910662. Epub 2019 May 1.
Three-dimensional structure-based localization aims to estimate the six-DOF camera pose of a query image by means of feature matches against a 3D Structure-from-Motion (SfM) point cloud. For city-scale SfM point clouds with tens of millions of points, it becomes more and more difficult to disambiguate matches. Therefore, a 3D structure-based localization method, which can efficiently handle matches with very large outlier ratios, is needed. We propose a two-stage outlier filtering framework for city-scale localization that leverages both visibility and geometry intrinsics of the SfM point clouds. First, we propose a visibility-based outlier filter, which is based on a bipartite visibility graph, to filter outliers on a coarse level. Second, we apply a geometry-based outlier filter to generate a set of fine-grained matches with a novel data-driven geometrical constraint for efficient inlier evaluation. The proposed two-stage outlier filtering framework only relies on the intrinsic information of the SfM point cloud. It is thus widely applicable to be embedded into the existing localization approaches. The experimental results on two real-world datasets demonstrate the effectiveness of the proposed two-stage outlier filtering framework for city-scale localization.
基于三维结构的定位旨在通过与三维运动恢复结构（SfM）点云进行特征匹配来估计查询图像的六自由度相机位姿。对于拥有数千万个点的城市规模SfM点云，区分匹配变得越来越困难。因此，需要一种基于三维结构的定位方法，它能够有效地处理具有非常大的离群值比例的匹配。我们提出了一种用于城市规模定位的两阶段离群值过滤框架，该框架利用了SfM点云的可见性和几何内在特性。首先，我们提出一种基于可见性的离群值过滤器，它基于二分可见性图，在粗粒度级别上过滤离群值。其次，我们应用一种基于几何的离群值过滤器，通过一种新颖的数据驱动几何约束生成一组细粒度匹配，以进行高效的内点评估。所提出的两阶段离群值过滤框架仅依赖于SfM点云的内在信息。因此，它广泛适用于嵌入到现有的定位方法中。在两个真实世界数据集上的实验结果证明了所提出的两阶段离群值过滤框架对于城市规模定位的有效性。
