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：对同一地点不同时间采集的机载LiDAR数据点进行无监督变化检测，因空间支撑不匹配及采集系统噪声而颇具挑战。当前大多数点云变化检测方法严重依赖数字高程模型图像计算与监督学习方法。获取数字高程模型会导致LiDAR数据因像素化而信息损失，而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。我们提出一种基于三维LiDAR点在两个时间支撑上传输计算的无监督方法。该方法基于非平衡最优传输理论，可泛化至任何LiDAR数据变化检测问题。我们将该方法应用于公开数据集，模拟实际中多种传感器的噪声与分辨率配置，监测城市扩展现象。该方法支持无监督多类分类，其性能显著优于现有最优无监督方法。