Unsupervised change detection between airborne LiDAR data points, taken at separate times over the same location, can be difficult due to unmatching spatial support and noise from the acquisition system. Most current approaches to detect changes in point clouds rely heavily on the computation of Digital Elevation Models (DEM) images and supervised methods. Obtaining a DEM leads to LiDAR informational loss due to pixelisation, and supervision requires large amounts of labelled data often unavailable in real-world scenarios. We propose an unsupervised approach based on the computation of the transport of 3D LiDAR points over two temporal supports. The method is based on unbalanced optimal transport and can be generalised to any change detection problem with LiDAR data. We apply our approach to publicly available datasets for monitoring urban sprawling in various noise and resolution configurations that mimic several sensors used in practice. Our method allows for unsupervised multi-class classification and outperforms the previous state-of-the-art unsupervised approaches by a significant margin.

翻译：机载LiDAR数据在不同时间对同一地点采集的点云进行无监督变化检测存在困难，主要原因在于空间支持不匹配和采集系统噪声干扰。现有的大多数点云变化检测方法高度依赖数字高程模型（DEM）图像的计算和有监督方法。获取DEM会导致LiDAR信息因像素化而损失，而有监督方法需要大量标注数据，这在现实场景中往往难以获得。我们提出了一种基于三维LiDAR点在两个时间支持域上传输计算的无监督方法。该方法基于非平衡最优传输，可推广至任何基于LiDAR数据的变化检测问题。我们将该方法应用于公开数据集，在模拟多种实际传感器使用的噪声和分辨率配置下监测城市扩张。该方法实现了无监督多类别分类，并以显著优势超越了先前最先进的无监督方法。