  Change detection from traditional \added{2D} optical images has limited
capability to model the changes in the height or shape of objects. Change
detection using 3D point cloud \added{from photogrammetry or LiDAR surveying}
can fill this gap by providing critical depth information. While most existing
machine learning based 3D point cloud change detection methods are supervised,
they severely depend on the availability of annotated training data, which is
in practice a critical point. To circumnavigate this dependence, we propose an
unsupervised 3D point cloud change detection method mainly based on
self-supervised learning using deep clustering and contrastive learning. The
proposed method also relies on an adaptation of deep change vector analysis to
3D point cloud via nearest point comparison. Experiments conducted on \added{an
aerial LiDAR survey dataset} show that the proposed method obtains higher
performance in comparison to the traditional unsupervised methods, with a gain
of about 9\% in mean accuracy (to reach more than 85\%). Thus, it appears to be
a relevant choice in scenario where prior knowledge (labels) is not ensured.
