# Towards Real-World Category-level Articulation Pose Estimation

arXiv: https://arxiv.org/abs/2105.03260 · HTML: https://mlsanity.com/paper/2105.03260/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019)
- [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://mlsanity.com/paper/1711.10006/) (2017)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image](https://mlsanity.com/paper/1812.04072/) (2018)
- [6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints](https://mlsanity.com/paper/1910.10750/) (2019)
- [Deep Part Induction from Articulated Object Pairs](https://mlsanity.com/paper/1809.07417/) (2018)
- [The RBO Dataset of Articulated Objects and Interactions](https://mlsanity.com/paper/1806.06465/) (2018)
- [LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes](https://mlsanity.com/paper/1707.04796/) (2017)
- [Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation](https://mlsanity.com/paper/2012.00088/) (2020)
- [Applying Domain Randomization to Synthetic Data for Object Category Detection](https://mlsanity.com/paper/1807.09834/) (2018)
- [CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning](https://mlsanity.com/paper/2003.05848/) (2020)

## Built on by (most cited first, 30 of 39)

- [AKB-48: A Real-World Articulated Object Knowledge Base](https://mlsanity.com/paper/2202.08432/) (2022)
- [Full-Body Articulated Human-Object Interaction](https://mlsanity.com/paper/2212.10621/) (2022)
- [ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis](https://mlsanity.com/paper/2109.05488/) (2021)
- [ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting](https://mlsanity.com/paper/2502.19459/) (2025)
- [Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling](https://mlsanity.com/paper/2502.02590/) (2025)
- [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [Understanding 3D Object Articulation in Internet Videos](https://mlsanity.com/paper/2203.16531/) (2022)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance](https://mlsanity.com/paper/2302.14268/) (2023)
- [Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis](https://mlsanity.com/paper/2406.16623/) (2024)
- [FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion](https://mlsanity.com/paper/2510.25765/) (2025)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [GarmentTracking: Category-Level Garment Pose Tracking](https://mlsanity.com/paper/2303.13913/) (2023)
- [ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation](https://mlsanity.com/paper/2305.01618/) (2023)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [LARM: A Large Articulated-Object Reconstruction Model](https://mlsanity.com/paper/2511.11563/) (2025)
- [CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer](https://mlsanity.com/paper/2402.17360/) (2024)
- [ART: Articulated Reconstruction Transformer](https://mlsanity.com/paper/2512.14671/) (2025)
- [RoSI: Recovering 3D Shape Interiors from Few Articulation Images](https://mlsanity.com/paper/2304.06342/) (2023)
- [MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos](https://mlsanity.com/paper/2505.11868/) (2025)
- [Articulation in Motion: Prior-free Part Mobility Analysis for Articulated Objects By Dynamic-Static Disentanglement](https://mlsanity.com/paper/2603.02910/) (2026)
- [RPMArt: Towards Robust Perception and Manipulation for Articulated Objects](https://mlsanity.com/paper/2403.16023/) (2024)
- [Discovering Conceptual Knowledge with Analytic Ontology Templates for Articulated Objects](https://mlsanity.com/paper/2409.11702/) (2024)
- [CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image](https://mlsanity.com/paper/2504.11230/) (2025)
- [Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects](https://mlsanity.com/paper/2605.24403/) (2026)
- [CA2T-Net: Category-Agnostic 3D Articulation Transfer from Single Image](https://mlsanity.com/paper/2301.02232/) (2023)
- [Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker](https://mlsanity.com/paper/2401.04377/) (2024)
- [You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping](https://mlsanity.com/paper/2506.05719/) (2025)

## Abstract (arXiv)

Human life is populated with articulated objects. Current Category-level Articulation Pose Estimation (CAPE) methods are studied under the single-instance setting with a fixed kinematic structure for each category. Considering these limitations, we reform this problem setting for real-world environments and suggest a CAPE-Real (CAPER) task setting. This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world. To support this task, we build an articulated model repository ReArt-48 and present an efficient dataset generation pipeline, which contains Fast Articulated Object Modeling (FAOM) and Semi-Authentic MixEd Reality Technique (SAMERT). Accompanying the pipeline, we build a large-scale mixed reality dataset ReArtMix and a real world dataset ReArtVal. We also propose an effective framework ReArtNOCS that exploits RGB-D input to estimate part-level pose for multiple instances in a single forward pass. Extensive experiments demonstrate that the proposed ReArtNOCS can achieve good performance on both CAPER and CAPE settings. We believe it could serve as a strong baseline for future research on the CAPER task.