3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark

Bare Luka Žagar, Mingyu Liu, Tim Hertel, Ekim Yurtsever, Alois C. Knoll; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6404-6414

Deformable linear objects are commonly found in our daily lives. Understanding them visually can be challenging even for humans as the same object can become entangled and look completely different. Examples of deformable linear objects include blood vessels and wiring harnesses which are crucial for the proper functioning of systems like the human body and vehicles. Recently some studies have focused on 2D image segmentation of wires. However there are no point cloud datasets available for studying 3D deformable linear objects which are more complex and challenging. To address this gap we introduce two point cloud datasets PointWire and PointVessel generated using our proposed semi-automatic pipeline. We evaluated state-of-the-art methods on these large-scale 3D deformable linear object benchmarks. Additionally we analyzed the generalization capabilities of these methods through transferability experiments on the PointWire and PointVessel datasets.