  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.
