mlsanityData to 7 Oct 2026

CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer

arXiv 2402.17360 ↗First posted 27 Feb 2024cs.CV cs.AI cs.ROcited by 11

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Abstract (arXiv)

The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud. The proposed CAPT methods accurately estimate joint parameters and states for various articulated objects with high precision and robustness. The paper also introduces a motion loss approach, which improves articulation estimation performance by emphasizing the dynamic features of articulated objects. Additionally, the paper presents a double voting strategy to provide the framework with coarse-to-fine parameter estimation. Experimental results on several category datasets demonstrate that our methods outperform existing alternatives for articulation estimation. Our research provides a promising solution for applying Transformer-based architectures in articulated object analysis.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

24 of 35 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2020PCT: Point cloud transformercited by 730
  5. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  6. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  7. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
  8. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  9. 2019Category-Level Articulated Object Pose Estimationcited by 158
  10. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 152
  11. 2019Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapescited by 117
  12. 2022FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objectscited by 91
12 more
  1. 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 83
  2. 2016Interactive Perception: Leveraging Action in Perception and Perception in Actioncited by 77
  3. 2020ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theorycited by 70
  4. 2018Deep Part Induction from Articulated Object Pairscited by 66
  5. 2022CLA-NeRF: Category-Level Articulated Neural Radiance Fieldcited by 55
  6. 2014A Probabilistic Framework for Learning Kinematic Models of Articulated Objectscited by 40
  7. 2021Towards Real-World Category-level Articulation Pose Estimationcited by 39
  8. 2022Category-Independent Articulated Object Tracking with Factor Graphscited by 24
  9. 2021Distributional Depth-Based Estimation of Object Articulation Modelscited by 18
  10. 2023Semi-Weakly Supervised Object Kinematic Motion Predictioncited by 14
  11. 2020A Hand Motion-guided Articulation and Segmentation Estimationcited by 2
  12. 2020Fast and Robust Bin-picking System for Densely Piled Industrial Objectscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 3.

11 papers.

  1. 2025FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusioncited by 22
  2. 2025LARM: A Large Articulated-Object Reconstruction Modelcited by 12
  3. 2025ART: Articulated Reconstruction Transformercited by 9
  4. 2025MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videoscited by 6
  5. 2025DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Predictioncited by 2
  6. 2024ManipGPT: Is Affordance Segmentation by Large Vision Models Enough for Articulated Object Manipulation?cited by 1
  7. 2025PAOLI: Pose-free Articulated Object Learning from Sparse-view Imagescited by 1
  8. 2026ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposalscited by 1
  9. 2026Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learningcited by 1
  10. 2025Canonical Space Representation for 4D Panoptic Segmentation of Articulated Objectscited by 0
  11. 2026FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observationscited by 0