mlsanityData to 7 Oct 2026

StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction

arXiv 2607.26889 ↗First posted 29 Jul 2026cs.GR cs.CV cs.ROcited by 0

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Abstract (arXiv)

Reconstructing articulated objects with multiple movable parts is essential for understanding object structure and enabling physical interaction. However, this reconstruction task poses significant challenges due to the entanglement of geometry, appearance, and motion parameters during optimization. Existing methods rely primarily on photometric supervision, which commonly fails to disentangle these interdependent components, resulting in poor part decomposition with blurred boundaries and geometric artifacts. To address this limitation, we introduce StructureGS, a reconstruction framework for articulated objects that integrates structure-aware guidance into 3D Gaussian Splatting. Our approach leverages oriented bounding boxes of object parts to enforce two key structural properties: spatial coherence, which constrains each part's geometry to remain compact and spatially coherent within its designated region, and structural connectivity, which enforces physically plausible contact relationships between adjacent parts. These properties are realized through structure-aware losses that inject explicit structural constraints into the optimization process. Extensive experiments demonstrate that our method achieves state-of-the-art performance in articulated object reconstruction, producing high-quality results with well-defined part geometries.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 40 references that are in the map. First posted on arXiv 29 Jul 2026.

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31 of 40 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  3. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
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  11. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  12. 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
19 more
  1. 2024gsplat: An Open-Source Library for Gaussian Splattingcited by 279
  2. 2023Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particlecited by 210
  3. 2019Category-Level Articulated Object Pose Estimationcited by 158
  4. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 152
  5. 2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 135
  6. 2023PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objectscited by 115
  7. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 113
  8. 2024Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objectscited by 67
  9. 2025ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splattingcited by 67
  10. 2023CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objectscited by 64
  11. 2022Self-supervised Neural Articulated Shape and Appearance Modelscited by 51
  12. 2024Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstructioncited by 51
  13. 2021Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objectscited by 50
  14. 2024REACTO: Reconstructing Articulated Objects from a Single Videocited by 45
  15. 2025Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modelingcited by 41
  16. 2025ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splattingcited by 35
  17. 2023Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariancecited by 26
  18. 2025SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splattingcited by 13
  19. 2025ScrewSplat: An End-to-End Method for Articulated Object Recognitioncited by 9

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