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.

本文提出了一种无监督方法 DeepCluster 3D Change Detection（DC3DCD），用于检测和分类多类变化，基于 3D 点云，并在模拟和真实数据集上进行了评估，结果深度聚类与标记方法相比具有较高的准确性和竞争性。