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.

翻译：变化检测是一项重要任务，能快速识别变化区域，尤其在处理多时相数据时。在地形复杂（如城市环境）的场景中，垂直信息是一种非常有用的知识来源，可以突出变化并将其分类为不同类别。在本研究中，我们专注于直接使用原始三维（3D）点云进行变化分割，以避免栅格化过程中的信息损失。虽然深度学习近期通过孪生网络编码信息证明了其在此任务上的有效性，我们在此探讨在深度网络早期阶段也使用变化信息的想法。为此，我们首先提出为孪生KPConv先进网络提供手工特征，特别是与变化相关的特征，这将在变化类别上的平均交并比提升4.70%。考虑到变化相关特征带来了显著改进，我们随后提出了三种新架构来解决3D点云变化分割：OneConvFusion、Triplet KPConv和Encoder Fusion SiamKPConv。所有这些网络在早期阶段都考虑变化信息，并超越了现有先进方法。特别是Encoder Fusion SiamKPConv在变化类别上的平均交并比超过现有先进方法5%以上，强调了网络聚焦于变化信息对变化检测任务的价值。代码可在https://github.com/IdeGelis/torch-points3d-SiamKPConvVariants获取。