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.

我们提出了一种新颖的Voxel-Cross-Pixel（VXP）方法，通过自监督方式建立了体素和像素之间的对应关系，并将其带入共享特征空间，从而解决了在不同领域（2D图像和3D点云）提取一致且鲁棒的全局描述符的挑战。通过在三个基准测试集（Oxford RobotCar、ViViD++和KITTI）上进行的大量实验证明，我们的方法在跨模态检索方面超越了现有技术的很大幅度。

VXP: 像素-体素大规模图像激光雷达地点识别