# DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2505.08644 · HTML: https://mlsanity.com/paper/2505.08644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 61 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 29 of 29)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [gsplat: An Open-Source Library for Gaussian Splatting](https://mlsanity.com/paper/2409.06765/) (2024)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [DynIBaR: Neural Dynamic Image-Based Rendering](https://mlsanity.com/paper/2211.11082/) (2022)
- [Monocular Dynamic View Synthesis: A Reality Check](https://mlsanity.com/paper/2210.13445/) (2022)
- [Tracking Everything Everywhere All at Once](https://mlsanity.com/paper/2306.05422/) (2023)
- [VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation](https://mlsanity.com/paper/2303.08340/) (2023)
- [DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions](https://mlsanity.com/paper/1906.03853/) (2019)
- [Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling](https://mlsanity.com/paper/2410.18912/) (2024)
- [DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation](https://mlsanity.com/paper/2312.00583/) (2023)
- [Occlusion-robust Deformable Object Tracking without Physics Simulation](https://mlsanity.com/paper/2101.00733/) (2021)
- [FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of Dynamic Scenes](https://mlsanity.com/paper/2309.05418/) (2023)
- [Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints](https://mlsanity.com/paper/2011.00627/) (2020)
- [Simultaneous Learning of Contact and Continuous Dynamics](https://mlsanity.com/paper/2310.12054/) (2023)
- [Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision](https://mlsanity.com/paper/2501.01715/) (2025)
- [Deformable Linear Object Prediction Using Locally Linear Latent Dynamics](https://mlsanity.com/paper/2103.14184/) (2021)
- [Disentangling Dense Multi-Cable Knots](https://mlsanity.com/paper/2106.02252/) (2021)
- [Learning Topological Motion Primitives for Knot Planning](https://mlsanity.com/paper/2009.02615/) (2020)
- [TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach](https://mlsanity.com/paper/2407.03245/) (2024)
- [Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models](https://mlsanity.com/paper/2109.11091/) (2021)
- [Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation](https://mlsanity.com/paper/2202.06172/) (2022)
- [MultiDLO: Simultaneous Shape Tracking of Multiple Deformable Linear Objects with Global-Local Topology Preservation](https://mlsanity.com/paper/2310.13245/) (2023)

## Abstract (arXiv)

This work presents DLO-Splatting, an algorithm for estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information through prediction-update filtering. The DLO-Splatting algorithm uses a position-based dynamics model with shape smoothness and rigidity dampening corrections to predict the object shape. Optimization with a 3D Gaussian Splatting-based rendering loss iteratively renders and refines the prediction to align it with the visual observations in the update step. Initial experiments demonstrate promising results in a knot tying scenario, which is challenging for existing vision-only methods.