mlsanityData to 14 Oct 2026

Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations

arXiv 2311.12407 ↗First posted 21 Nov 2023cs.CVcited by 5

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Abstract (arXiv)

Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometries, semantics, and part functions. Modeling different kinds of parts and articulations with nerual networks plays an essential role in articulated object understanding and manipulation, and will further benefit 3D vision and robotics communities. To model articulated objects, most previous works directly encode articulated objects into feature representations, without specific designs for parts, articulations and part motions. In this paper, we introduce a novel framework that explicitly disentangles the part motion of articulated objects by predicting the transformation matrix of points on the part surface, using spatially continuous neural implicit representations to model the part motion smoothly in the space. More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space. Quantitative and qualitative results of experiments over diverse categories of articulated objects demonstrate the effectiveness of our proposed framework.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

32 of 46 references.

References · 32

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  8. 2020Convolutional Occupancy Networkscited by 761
  9. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  10. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 595
  11. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
  12. 2020Local Implicit Grid Representations for 3D Scenescited by 373
20 more
  1. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 182
  2. 2019Category-Level Articulated Object Pose Estimationcited by 159
  3. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 156
  4. 2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 139
  5. 2019Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapescited by 119
  6. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 114
  7. 2022FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objectscited by 91
  8. 2021VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objectscited by 80
  9. 2020ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theorycited by 70
  10. 2021UMPNet: Universal Manipulation Policy Network for Articulated Objectscited by 65
  11. 2021AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactionscited by 57
  12. 2021MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronizationcited by 46
  13. 2021Act the Part: Learning Interaction Strategies for Articulated Object Part Discoverycited by 45
  14. 2023PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observationscited by 42
  15. 2014A Probabilistic Framework for Learning Kinematic Models of Articulated Objectscited by 40
  16. 2020Visual Identification of Articulated Object Partscited by 37
  17. 2023Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objectscited by 35
  18. 2020Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimationcited by 25
  19. 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusionscited by 23
  20. 2018Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagationcited by 16

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondencecited by 23
  2. 2025AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learningcited by 12
  3. 2025Learning Human-Humanoid Coordination for Collaborative Object Carryingcited by 7
  4. 2024EqvAfford: SE(3) Equivariance for Point-Level Affordance Learningcited by 6
  5. 2025MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videoscited by 6