  Accurately and robustly estimating the state of deformable linear objects
(DLOs), such as ropes and wires, is crucial for DLO manipulation and other
applications. However, it remains a challenging open issue due to the high
dimensionality of the state space, frequent occlusions, and noises. This paper
focuses on learning to robustly estimate the states of DLOs from single-frame
point clouds in the presence of occlusions using a data-driven method. We
propose a novel two-branch network architecture to exploit global and local
information of input point cloud respectively and design a fusion module to
effectively leverage the advantages of both methods. Simulation and real-world
experimental results demonstrate that our method can generate globally smooth
and locally precise DLO state estimation results even with heavily occluded
point clouds, which can be directly applied to real-world robotic manipulation
of DLOs in 3-D space.
