A Robust Deformable Linear Object Perception Pipeline in 3D: From Segmentation to Reconstruction

3D perception of deformable linear objects (DLOs) is crucial for DLO manipulation. However, perceiving DLOs in 3D from a single RGBD image is challenging. Previous DLO perception methods fail to extract a decent 3D DLO model due to different textures, occlusions, sparse and false depth information. To address these problems and provide a more robust DLO perception initialization for downstream tasks like tracking and manipulation in complex scenarios, this paper proposes a 3D DLO perception pipeline to first segment a DLO in 2D images and post-process masks to eliminate false positive segmentation, reconstruct the DLO in 3D space to predict the occluded part of the DLO, and physically smooth the reconstructed DLO. By testing on a synthetic DLO dataset and further validating on a real-world dataset with seven different DLOs, we demonstrate that the proposed method is an effective and robust 3D perception pipeline solution with better performance on 2D DLO segmen- tation and 3D DLO reconstruction compared to State-of-the-Art algorithms.