  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.
