The rapid development of 3D acquisition devices enables us to collect billions of points in a few hours. However, the analysis of the output data is a challenging task, especially in the field of 3D point cloud change detection. In this Shape Retrieval Challenge (SHREC) track, we provide a street-scene dataset for 3D point cloud change detection. The dataset consists of 866 3D object pairs in year 2016 and 2020 from 78 large-scale street scene 3D point clouds. Our goal is to detect the changes from multi-temporal point clouds in a complex street environment.

We compare three methods on this benchmark, with one handcrafted (PoChaDeHH) and the other two learning-based (HGI-CD and SiamGCN). The results show that the handcrafted algorithm has balanced performance over all classes, while learning-based methods achieve overwhelming performance but suffer from the class-imbalanced problem and may fail on minority classes. The randomized oversampling metric applied in SiamGCN can alleviate this problem. Also, different siamese network architecture in HGI-CD and SiamGCN contribute to the designing of a network for the 3D change detection task.

3D 采集设备的快速发展使我们能够在几小时内采集数十亿个点。然而，输出数据的分析是一项具有挑战性的任务，尤其是在 3D 点云变化检测领域。在此形状检索挑战 (SHREC) 赛道中，我们提供了用于 3D 点云变化检测的街景数据集。该数据集由 2016 年和 2020 年的 866 个 3D 对象对组成，来自 78 个大型街道场景 3D 点云。我们的目标是检测复杂街道环境中多时态点云的变化。

我们在这个基准测试中比较了三种方法，一种是手工制作的（PoChaDeHH），另两种是基于学习的（HGI-CD 和 SiamGCN）。结果表明，手工算法在所有类上都具有平衡的性能，而基于学习的方法实现了压倒性的性能，但存在类不平衡问题，并且可能在少数类上失败。SiamGCN 中应用的随机过采样度量可以缓解这个问题。此外，HGI-CD 和 SiamGCN 中不同的 siamese 网络架构有助于设计用于 3D 变化检测任务的网络。