  Recent works on the global place recognition treat the task as a retrieval
problem, where an off-the-shelf global descriptor is commonly designed in
image-based and LiDAR-based modalities. However, it is non-trivial to perform
accurate image-LiDAR global place recognition since extracting consistent and
robust global descriptors from different domains (2D images and 3D point
clouds) is challenging. To address this issue, we propose a novel
Voxel-Cross-Pixel (VXP) approach, which establishes voxel and pixel
correspondences in a self-supervised manner and brings them into a shared
feature space. Specifically, VXP is trained in a two-stage manner that first
explicitly exploits local feature correspondences and enforces similarity of
global descriptors. Extensive experiments on the three benchmarks (Oxford
RobotCar, ViViD++ and KITTI) demonstrate our method surpasses the
state-of-the-art cross-modal retrieval by a large margin.
