mlsanityData to 7 Oct 2026

OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation

arXiv 2408.16547 ↗First posted 29 Aug 2024cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

Category-level articulated object pose estimation focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to solve this task. Our model consistently generates reconstruction with a canonical pose and joint state for the entire input object, and it estimates object-level poses that reduce overall pose variance and part-level poses that align each part of the input with its corresponding part of the reconstruction. Experimental results demonstrate that our approach significantly outperforms previous self-supervised methods and is comparable to the state-of-the-art supervised methods. To assess the performance of our model in real-world scenarios, we also introduce a new real-world articulated object benchmark dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 39 of its 49 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

39 of 49 references.

References · 39

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2023Segment Anythingcited by 8,158
  3. 2017Mask R-CNNcited by 7,259
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  7. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  8. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  9. 2020Object-Centric Learning with Slot Attentioncited by 865
  10. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  11. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
  12. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
27 more
  1. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 490
  2. 2022HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interactioncited by 321
  3. 2019Category-Level Articulated Object Pose Estimationcited by 158
  4. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 158
  5. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 152
  6. 2021FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanismcited by 121
  7. 2019Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapescited by 117
  8. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 113
  9. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 111
  10. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 101
  11. 2022GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Votingcited by 94
  12. 2018Unsupervised Learning of Shape and Pose with Differentiable Point Cloudscited by 89
  13. 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 83
  14. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  15. 2020Self6D: Self-Supervised Monocular 6D Object Pose Estimationcited by 79
  16. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 70
  17. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 69
  18. 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 56
  19. 2022OPD: Single-view 3D Openable Part Detectioncited by 52
  20. 2018A Unified Framework for Multi-View Multi-Class Object Pose Estimationcited by 49
  21. 2023HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimationcited by 47
  22. 2021MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronizationcited by 46
  23. 2021Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimationcited by 45
  24. 2023Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariancecited by 26
  25. 2020Self-Supervised Learning of Part Mobility from Point Cloud Sequencecited by 18
  26. 2022E2PN: Efficient SE(3)-Equivariant Point Networkcited by 17
  27. 2023Semi-Weakly Supervised Object Kinematic Motion Predictioncited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videoscited by 6
  2. 2026DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spacescited by 2
  3. 2025PAOLI: Pose-free Articulated Object Learning from Sparse-view Imagescited by 1
  4. 2026ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposalscited by 1
  5. 2026COG: Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object Pose Estimationcited by 1
  6. 2026DailyArt: Discovering Articulation from Single Static Images via Latent Dynamicscited by 0
  7. 2026Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Datacited by 0
  8. 2026SCAPO: Self-Supervised Category-Level Articulated Pose Estimation from a Single 3D Observationcited by 0