# DLOFTBs -- Fast Tracking of Deformable Linear Objects with B-splines

arXiv: https://arxiv.org/abs/2302.13694 · HTML: https://mlsanity.com/paper/2302.13694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints](https://mlsanity.com/paper/2011.00627/) (2020)
- [Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation](https://mlsanity.com/paper/1810.04461/) (2018)
- [Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model](https://mlsanity.com/paper/2004.05233/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2309.07609/) (2023)

## Abstract (arXiv)

While manipulating rigid objects is an extensively explored research topic, deformable linear object (DLO) manipulation seems significantly underdeveloped. A potential reason for this is the inherent difficulty in describing and observing the state of the DLO as its geometry changes during manipulation. This paper proposes an algorithm for fast-tracking the shape of a DLO based on the masked image. Having no prior knowledge about the tracked object, the proposed method finds a reliable representation of the shape of the tracked object within tens of milliseconds. This algorithm's main idea is to first skeletonize the DLO mask image, walk through the parts of the DLO skeleton, arrange the segments into an ordered path, and finally fit a B-spline into it. Experiments show that our solution outperforms the State-of-the-Art approaches in DLO's shape reconstruction accuracy and algorithm running time and can handle challenging scenarios such as severe occlusions, self-intersections, and multiple DLOs in a single image.