论文标题
Croco：跨模式对比度学习用于地球观测数据的本地化
CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data
论文作者
论文摘要
在遥感图像上将地面的LiDar Point Cloud定位是很有趣的。在这项工作中，我们解决了此问题的子任务，即绘制从空中图像上的空中激光雷德点云隔离的数字高程模型（DEM）。我们提出了一种基于对比的学习方法，该方法训练DEM和高分辨率光学图像，并在不同的数据采样策略和超参数上试验框架。在最佳情况下，获得了0.71的前1个得分，前5个得分为0.81。所提出的方法有望从RGB和DEM进行特征学习以进行本地化，并且也可能适用于其他数据源。源代码将在https://github.com/wtseng530/avlocalization上发布。
It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the aerial imagery. We proposed a contrastive learning-based method that trains on DEM and high-resolution optical imagery and experiment the framework on different data sampling strategies and hyperparameters. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed method is promising for feature learning from RGB and DEM for localization and is potentially applicable to other data sources too. Source code will be released at https://github.com/wtseng530/AVLocalization.
