In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images. In this context, 3D point clouds (PCs) obtained by LiDAR or photogrammetry are very interesting. While recent studies showed the considerable benefit of using deep learning-based methods to detect and characterize changes into raw 3D PCs, these studies rely on large annotated training data to obtain accurate results. The collection of these annotations are tricky and time-consuming. The availability of unsupervised or weakly supervised approaches is then of prime interest. In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level. We classify our approach in the unsupervised family given the fact that we extract in a completely unsupervised way a number of clusters associated with potential changes. Let us precise that in the end of the process, the user has only to assign a label to each of these clusters to derive the final change map. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs and perform change segmentation task. An assessment of the method on both simulated and real public dataset is provided. The proposed method allows to outperform fully-supervised traditional machine learning algorithm and to be competitive with fully-supervised deep learning networks applied on rasterization of 3D PCs with a mean of IoU over classes of change of 57.06% and 66.69% for the simulated and the real datasets, respectively.

翻译：在持续变化的世界中，变化检测对于保持地图的实时更新至关重要。为了更好地感知具有复杂几何结构的区域（尤其是城市区域），三维数据成为传统二维图像的有趣替代方案。在此背景下，通过激光雷达或摄影测量获取的三维点云（PCs）具有重要价值。尽管近期研究表明，基于深度学习的方法在检测和表征原始三维点云变化方面具有显著优势，但这些研究依赖大规模标注训练数据才能获得精确结果，而此类标注的收集既困难又耗时。因此，无监督或弱监督方法的可用性成为关键需求。本文提出一种名为DeepCluster三维变化检测（DC3DCD）的无监督方法，用于在点级别检测和分类多类变化。鉴于我们以完全无监督的方式提取与潜在变化相关的聚类数量，我们将该方法归类为无监督方法。需要说明的是，在流程结束时，用户只需为每个聚类分配标签即可生成最终变化图。该方法基于原始为图像分类设计的DeepCluster技术，经过改进后能够处理复杂的原始三维点云并执行变化分割任务。我们提供了该方法在模拟数据集和真实公开数据集上的评估结果。所提方法不仅优于全监督传统机器学习算法，还能与基于三维点云栅格化的全监督深度学习网络相竞争，在模拟数据集和真实数据集上的变化类别平均交并比（IoU）分别达到57.06%和66.69%。