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

VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition

We tackle the problem of 3D point cloud localization based on a few natural
linguistic descriptions and introduce a novel neural network, Text2Loc, that
fully interprets the semantic relationship between points and text. Text2Loc
follows a coarse-to-fine localization pipeline: text-submap global place
recognition, followed by fine localization. In global place recognition,
relational dynamics among each textual hint are captured in a hierarchical
transformer with max-pooling (HTM), whereas a balance between positive and
negative pairs is maintained using text-submap contrastive learning. Moreover,
we propose a novel matching-free fine localization method to further refine the
location predictions, which completely removes the need for complicated
text-instance matching and is lighter, faster, and more accurate than previous
methods. Extensive experiments show that Text2Loc improves the localization
accuracy by up to $2\times$ over the state-of-the-art on the KITTI360Pose
dataset. We will make the code publicly available.

我们提出了一种新型的神经网络 Text2Loc，通过几个自然语言描述实现了基于 3D 点云定位，并完全解释了点与文本之间的语义关系，实现了粗 - 细定位的本地化过程，文本子图全局位置识别中，用分层 Transformer 和最大池化捕获了每个文本提示之间的关系动态，而使用文本子图对比学习保持了正负样本之间的平衡；此外，我们还提出了一种新颖的无匹配精细定位方法，进一步提升了位置预测的准确性，摆脱了复杂的文本实例匹配需求，并且比之前的方法更轻量化、更快速、更准确。大量实验证明，Text2Loc 在 KITTI360Pose 数据集上超过了当前最先进技术，定位准确度提升了 2 倍。我们将公开提供代码。