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), whether obtained through LiDAR or photogrammetric techniques, provide valuable information. 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：在持续演变的世界中，变化检测对于保持地图时效性至关重要。为更好感知复杂几何区域（尤其是城市区域），三维数据作为经典二维图像的替代方案展现出重要价值。在此背景下，通过激光雷达或摄影测量技术获取的三维点云提供了宝贵信息。尽管近期研究表明，基于深度学习的方法在原始三维点云变化检测与表征方面具有显著优势，但这些研究依赖大量标注训练数据方能获得精确结果。此类标注数据的采集复杂且耗时，因此无监督或弱监督方法的实用性备受关注。本文提出一种名为DeepCluster三维变化检测（DC3DCD）的无监督方法，用于在点级别检测与分类多类别变化。我们将该方法归入无监督学习家族，因其以完全无监督方式提取与潜在变化相关的若干聚类。需说明的是，最终处理过程中，用户只需为每个聚类分配标签即可生成最终变化图。所提方法基于最初为图像分类设计的DeepCluster方法，通过改进使其适用于复杂原始三维点云并执行变化分割任务。本文分别在模拟数据集和真实公开数据集上进行了方法评估。实验结果表明，该方法不仅优于全监督传统机器学习算法，且在三维点云栅格化数据上，与全监督深度学习网络相比具有竞争力：在模拟数据集和真实数据集上，各类别变化交并比均值分别达到57.06%和66.69%。