# Distributional Depth-Based Estimation of Object Articulation Models

arXiv: https://arxiv.org/abs/2108.05875 · HTML: https://mlsanity.com/paper/2108.05875/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [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)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [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)
- [Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction](https://mlsanity.com/paper/1906.03631/) (2019)
- [SE-Sync: A Certifiably Correct Algorithm for Synchronization over the Special Euclidean Group](https://mlsanity.com/paper/1612.07386/) (2016)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory](https://mlsanity.com/paper/2008.10518/) (2020)
- [Learning for Safety-Critical Control with Control Barrier Functions](https://mlsanity.com/paper/1912.10099/) (2019)
- [A Probabilistic Framework for Learning Kinematic Models of Articulated Objects](https://mlsanity.com/paper/1405.7705/) (2014)
- [RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud](https://mlsanity.com/paper/2006.14865/) (2020)
- [Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation](https://mlsanity.com/paper/2012.00088/) (2020)
- [Learning Articulated Motions From Visual Demonstration](https://mlsanity.com/paper/1502.01659/) (2015)
- [Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty](https://mlsanity.com/paper/1907.09014/) (2019)
- [Inferring geometric constraints in human demonstrations](https://mlsanity.com/paper/1810.00140/) (2018)
- [Unscented Orientation Estimation Based on the Bingham Distribution](https://mlsanity.com/paper/1311.5796/) (2013)
- [Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics](https://mlsanity.com/paper/1802.04205/) (2018)

## Built on by (most cited first, 18 of 18)

- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [OPDMulti: Openable Part Detection for Multiple Objects](https://mlsanity.com/paper/2303.14087/) (2023)
- [NAP: Neural 3D Articulation Prior](https://mlsanity.com/paper/2305.16315/) (2023)
- [Category-Independent Articulated Object Tracking with Factor Graphs](https://mlsanity.com/paper/2205.03721/) (2022)
- [Articulated Object Estimation in the Wild](https://mlsanity.com/paper/2509.01708/) (2025)
- [CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer](https://mlsanity.com/paper/2402.17360/) (2024)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [Inferring Articulated Rigid Body Dynamics from RGBD Video](https://mlsanity.com/paper/2203.10488/) (2022)
- [ScrewSplat: An End-to-End Method for Articulated Object Recognition](https://mlsanity.com/paper/2508.02146/) (2025)
- [ART: Articulated Reconstruction Transformer](https://mlsanity.com/paper/2512.14671/) (2025)
- [Articulated 3D Human-Object Interactions from RGB Videos: An Empirical Analysis of Approaches and Challenges](https://mlsanity.com/paper/2209.05612/) (2022)
- [Pandora: Articulated 3D Scene Graphs from Egocentric Vision](https://mlsanity.com/paper/2603.28732/) (2026)
- [SM3: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects](https://mlsanity.com/paper/2401.09133/) (2024)
- [Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery](https://mlsanity.com/paper/2207.08997/) (2022)
- [PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos](https://mlsanity.com/paper/2603.25539/) (2026)
- [Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects](https://mlsanity.com/paper/2505.06363/) (2025)
- [ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals](https://mlsanity.com/paper/2602.22666/) (2026)
- [DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics](https://mlsanity.com/paper/2604.07758/) (2026)

## Abstract (arXiv)

We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori. By contrast, existing methods that learn articulation models from raw observations typically only predict point estimates of the model parameters, which are insufficient to guarantee the safe manipulation of articulated objects. Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds. Combining these concepts allows for an efficient, mathematically sound representation that implicitly satisfies the constraints that rigid body transformations and articulations must adhere to. Leveraging this representation, we introduce a novel deep learning based approach, DUST-net, that performs category-independent articulation model estimation while also providing model uncertainties. We evaluate our approach on several benchmarking datasets and real-world objects and compare its performance with two current state-of-the-art methods. Our results demonstrate that DUST-net can successfully learn distributions over articulation models for novel objects across articulation model categories, which generate point estimates with better accuracy than state-of-the-art methods and effectively capture the uncertainty over predicted model parameters due to noisy inputs. Project webpage: https://pearl-utexas.github.io/DUST-net/