Leveraging Local and Global Descriptors in Parallel to Search Correspondences for Visual Localization

Abstract Visual localization to compute 6DoF camera pose from a given image has wide applications. Both local and global descriptors are crucial for visual localization. Most of the existing visual localization methods adopt a two-stage strategy: image retrieval first is performed by global descriptors, and then 2D-3D correspondences are made by local descriptors from 2D query image points and its nearest neighbor candidates which are the 3D points visible by these retrieved images. The above two stages are serially performed in these methods. However, due to the fact that 3D points obtained from the retrieval feedback are only rely on global descriptors, these methods cannot fully take the advantages of both local and global descriptors. In this paper, we propose a novel parallel search framework, which fully leverages advantages of both local and global descriptors to get nearest neighbor candidates of a 2D query image point. Specifically, besides using deep learning based global descriptors, we also utilize local descriptors to construct random tree structures for obtaining nearest neighbor candidates of the 2D query image point. We propose a new probability model and a new deep learning based local descriptor when constructing the random trees. In addition, a weighted Hamming regularization term to keep discriminativeness after binarization is given in loss function for the proposed local descriptor. The loss function co-trains both real and binary local descriptors of which the results are integrated into the random trees. Experiments on challenging benchmarks show that the proposed localization method can significantly improve the robustness and accuracy compared with the ones which get nearest neighbor candidates of a query local feature just based on either local or global descriptors.