# SPLATART: Articulated Gaussian Splatting with Estimated Object Structure

arXiv: https://arxiv.org/abs/2506.12184 · HTML: https://mlsanity.com/paper/2506.12184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [GART: Gaussian Articulated Template Models](https://mlsanity.com/paper/2311.16099/) (2023)
- [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023)
- [Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects](https://mlsanity.com/paper/2404.01440/) (2024)
- [Real2Code: Reconstruct Articulated Objects via Code Generation](https://mlsanity.com/paper/2406.08474/) (2024)
- [ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting](https://mlsanity.com/paper/2502.19459/) (2025)
- [CLA-NeRF: Category-Level Articulated Neural Radiance Field](https://mlsanity.com/paper/2202.00181/) (2022)
- [Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation](https://mlsanity.com/paper/2210.10108/) (2022)
- [GraspSplats: Efficient Manipulation with 3D Feature Splatting](https://mlsanity.com/paper/2409.02084/) (2024)
- [A Probabilistic Framework for Learning Kinematic Models of Articulated Objects](https://mlsanity.com/paper/1405.7705/) (2014)
- [ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting](https://mlsanity.com/paper/2503.08135/) (2025)
- [LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes](https://mlsanity.com/paper/1707.04796/) (2017)
- [Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis](https://mlsanity.com/paper/2406.16623/) (2024)
- [LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation](https://mlsanity.com/paper/2409.06703/) (2024)
- [Parts-Based Articulated Object Localization in Clutter Using Belief Propagation](https://mlsanity.com/paper/2008.02881/) (2020)
- [NARF22: Neural Articulated Radiance Fields for Configuration-Aware Rendering](https://mlsanity.com/paper/2210.01166/) (2022)
- [Understanding URDF: A Dataset and Analysis](https://mlsanity.com/paper/2308.00514/) (2023)
- [ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception](https://mlsanity.com/paper/2203.00283/) (2022)
- [Topologically-Informed Atlas Learning](https://mlsanity.com/paper/2110.00429/) (2021)
- [NARF24: Estimating Articulated Object Structure for Implicit Rendering](https://mlsanity.com/paper/2409.09829/) (2024)

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

- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)

## Abstract (arXiv)

Representing articulated objects remains a difficult problem within the field of robotics. Objects such as pliers, clamps, or cabinets require representations that capture not only geometry and color information, but also part seperation, connectivity, and joint parametrization. Furthermore, learning these representations becomes even more difficult with each additional degree of freedom. Complex articulated objects such as robot arms may have seven or more degrees of freedom, and the depth of their kinematic tree may be notably greater than the tools, drawers, and cabinets that are the typical subjects of articulated object research. To address these concerns, we introduce SPLATART - a pipeline for learning Gaussian splat representations of articulated objects from posed images, of which a subset contains image space part segmentations. SPLATART disentangles the part separation task from the articulation estimation task, allowing for post-facto determination of joint estimation and representation of articulated objects with deeper kinematic trees than previously exhibited. In this work, we present data on the SPLATART pipeline as applied to the syntheic Paris dataset objects, and qualitative results on a real-world object under spare segmentation supervision. We additionally present on articulated serial chain manipulators to demonstrate usage on deeper kinematic tree structures.