Image-based localization (IBL) aims to estimate the 6DOF camera pose for a given query image. The camera pose can be computed from 2D-3D matches between a query image and Structure-from-Motion (SfM) models. Despite recent advances in IBL, it remains difficult to simultaneously resolve the memory consumption and match ambiguity problems of large SfM models. In this work, we propose a cascaded parallel filtering method that leverages the feature, visibility and geometry information to filter wrong matches under binary feature representation. The core idea is that we divide the challenging filtering task into two parallel tasks before deriving an auxiliary camera pose for final filtering. One task focuses on preserving potentially correct matches, while another focuses on obtaining high quality matches to facilitate subsequent more powerful filtering. Moreover, our proposed method improves the localization accuracy by introducing a quality-aware spatial reconfiguration method and a principal focal length enhanced pose estimation method. Experimental results on real-world datasets demonstrate that our method achieves very competitive localization performances in a memory-efficient manner.

本篇论文提出了一种基于级联并行滤波方法来提高图片本地化的准确度和内存使用率。该方法针对大型Structure-from-Motion模型中可能出现的内存消耗和匹配模糊问题，通过特征、可见性和几何信息来过滤错误的匹配，并提高空间重构的质量和焦距估计的精度，实现了内存紧凑且性能优异的本地化表现。

内存高效的基于图像的级联并行滤波定位