  Change detection is an important task that rapidly identifies modified areas,
particularly when multi-temporal data are concerned. In landscapes with a
complex geometry (e.g., urban environment), vertical information is a very
useful source of knowledge that highlights changes and classifies them into
different categories. In this study, we focus on change segmentation using raw
three-dimensional (3D) point clouds (PCs) directly to avoid any information
loss due to the rasterization processes. While deep learning has recently
proven its effectiveness for this particular task by encoding the information
through Siamese networks, we investigate herein the idea of also using change
information in the early steps of deep networks. To do this, we first propose
to provide a Siamese KPConv state-of-the-art (SoTA) network with hand-crafted
features, especially a change-related one, which improves the mean of the
Intersection over Union (IoU) over the classes of change by 4.70%. Considering
that a major improvement is obtained due to the change-related feature, we then
propose three new architectures to address 3D PC change segmentation:
OneConvFusion, Triplet KPConv, and Encoder Fusion SiamKPConv. All these
networks consider the change information in the early steps and outperform the
SoTA methods. In particular, Encoder Fusion SiamKPConv overtakes the SoTA
approaches by more than 5% of the mean of the IoU over the classes of change,
emphasizing the value of having the network focus on change information for the
change detection task. The code is available at
https://github.com/IdeGelis/torch-points3d-SiamKPConvVariants.
