Change detection is an important task to rapidly identify modified areas, in
particular when multi-temporal data are concerned. In landscapes with complex
geometry such as urban environment, vertical information turn out to be a very
useful knowledge not only to highlight changes but also to classify them into
different categories. In this paper, we focus on change segmentation directly
using raw 3D point clouds (PCs), to avoid any loss of information due to
rasterization processes. While deep learning has recently proved its
effectiveness for this particular task by encoding the information through
Siamese networks, we investigate here the idea of also using change information
in early steps of deep networks. To do this, we first propose to provide the
Siamese KPConv State-of-The-Art (SoTA) network with hand-crafted features and
especially a change-related one. This improves the mean of Intersection over
Union (IoU) over classes of change by 4.70\%. Considering that the major
improvement was obtained thanks to the change-related feature, we propose three
new architectures to address 3D PCs change segmentation: OneConvFusion, Triplet
KPConv, and Encoder Fusion SiamKPConv. All the three networks take into account
change information in early steps and outperform SoTA methods. In particular,
the last network, entitled Encoder Fusion SiamKPConv, overtakes SoTA with more
than 5% of mean of IoU over classes of change emphasizing the value of having
the network focus on change information for change detection task.

本文提出了一种基于 3D 点云数据的变化检测方法 ——Encoder Fusion SiamKPConv 网络，通过对 Siamese KPConv 网络提供手工特征和变化相关特征，该方法在提高交并比的同时，避免了栅格化过程中的信息损失，优于当前 SoTA 方法。