SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting

arXiv:2506.03594 · cs.CV, cs.GR, cs.LG, cs.MM, cs.RO · Submitted 2025-06-04 · Read on arXiv

cs.CV, cs.GR, cs.LG, cs.MM, cs.RO

Submitted: 2025-06-04

Updated: 2025-06-04

Code: https://github.com/ripl/splart

Terminology

• Consistency — 28× in this paper · explained in 3 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Gaussian Splatting — 13× in this paper · explained in 4 episodes
• Robustness — 11× in this paper · explained in 5 episodes
• 3D Gaussian Splatting — 10× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• 3D Gaussian Splatting (3DGS) — 3× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Digital Twin — 3× in this paper · explained in 5 episodes
• European Conference on Computer Vision — 3× in this paper
• IEEE International Conference on Robotics and Automation — 3× in this paper
• International Conference on Computer Vision — 3× in this paper
• Point Clouds — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Angular Error — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Proceedings of the Conference on Robot Learning — 1× in this paper
• Vision-language models — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• ShapeNet: An Information-Rich 3D Model Repository
• Real2Code: Reconstruct Articulated Objects via Code Generation
• Neural Fields in Robotics: A Survey
• Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model
• NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
• Towards Massive Interaction with Generalist Robotics: A Systematic Review of XR-enabled Remote Human-Robot Interaction Systems
• RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
• Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery
• SAM 2: Segment Anything in Images and Videos
• Deep Part Induction from Articulated Object Pairs

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