3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
This addresses a data gap for researchers in computer vision and robotics working with deformable objects like blood vessels and wiring harnesses, though it is incremental as it focuses on dataset creation and benchmarking.
The paper tackled the lack of point cloud datasets for 3D deformable linear objects by introducing two large-scale datasets, PointWire and PointVessel, and evaluated state-of-the-art methods on these benchmarks, including transferability experiments.
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.