Memory efficient large-scale image-based localization

Local features have been widely used in the area of image-based localization. However, large-scale 2D-to-3D matching problems still involve massive memory consumption, which is mainly caused by the high dimensionality of the features (e.g. 128 dimensions of SIFT feature). This paper introduces a new method that decreases local features' high dimensionality for reducing memory capacity and accelerating the descriptor matching process. With this new method, all descriptors are projected into a lower dimensional space through the new learned matrices that are able to reduce the curse of dimensionality in the large scale image-based localization. The low dimensional descriptors are then mapped into a Hamming space for further reducing the memory requirement. This study also proposes an image-based localization pipeline based on the new learned Hamming descriptors. The new learned descriptor and the localization pipeline are applied to two challenging datasets. The experimental results show that the proposed method achieves extraordinary image registration performance compared with the published results from state-of-the-art methods.