Deformable linear objects are vastly represented in our everyday lives. It is often challenging even for humans to visually understand them, as the same object can be entangled so that it appears completely different. Examples of deformable linear objects include blood vessels and wiring harnesses, vital to the functioning of their corresponding systems, such as the human body and a vehicle. However, no point cloud datasets exist for studying 3D deformable linear objects. Therefore, we are introducing two point cloud datasets, PointWire and PointVessel. We evaluated state-of-the-art methods on the proposed large-scale 3D deformable linear object benchmarks. Finally, we analyzed the generalization capabilities of these methods by conducting transferability experiments on the PointWire and PointVessel datasets.

翻译：可变形线性物体在我们的日常生活中广泛存在。即使是人类，也很难通过视觉理解它们，因为同一物体可能因缠绕而呈现完全不同的形态。可变形线性物体的实例包括血管和线束，它们对相应系统（如人体和车辆）的正常运行至关重要。然而，目前尚不存在用于研究三维可变形线性物体的点云数据集。为此，我们引入了两个点云数据集：PointWire和PointVessel。我们在所提出的大规模三维可变形线性物体基准上评估了当前最先进的方法。最后，通过分别在PointWire和PointVessel数据集上进行迁移性实验，我们分析了这些方法的泛化能力。