# CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects

arXiv: https://arxiv.org/abs/2404.14968 · HTML: https://mlsanity.com/paper/2404.14968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 25 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [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)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)
- [CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://mlsanity.com/paper/2203.01929/) (2022)
- [Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation](https://mlsanity.com/paper/2403.08605/) (2024)
- [UMPNet: Universal Manipulation Policy Network for Articulated Objects](https://mlsanity.com/paper/2109.05668/) (2021)
- [CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects](https://mlsanity.com/paper/2303.15782/) (2023)
- [FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection](https://mlsanity.com/paper/2306.12893/) (2023)
- [Neural Grasp Distance Fields for Robot Manipulation](https://mlsanity.com/paper/2211.02647/) (2022)
- [Category-Independent Articulated Object Tracking with Factor Graphs](https://mlsanity.com/paper/2205.03721/) (2022)
- [SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo](https://mlsanity.com/paper/2106.16118/) (2021)
- [Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation](https://mlsanity.com/paper/2201.11924/) (2022)
- [Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation](https://mlsanity.com/paper/2307.06125/) (2023)
- [CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation](https://mlsanity.com/paper/2312.08240/) (2023)
- [Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing](https://mlsanity.com/paper/2309.16343/) (2023)
- [Kineverse: A Symbolic Articulation Model Framework for Model-Agnostic Mobile Manipulation](https://mlsanity.com/paper/2012.05362/) (2020)

## Built on by (most cited first, 1 of 1)

- [ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions](https://mlsanity.com/paper/2603.25791/) (2026)

## Abstract (arXiv)

Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D shape reconstruction and 6-DoF grasp estimation of articulated objects. CenterArt takes RGB-D images of the scene as input and first predicts the shape and joint codes through an encoder. The decoder then leverages these codes to reconstruct 3D shapes and estimate 6-DoF grasp poses of the objects. We further develop a mechanism for generating a dataset of 6-DoF grasp ground truth poses for articulated objects. CenterArt is trained on realistic scenes containing multiple articulated objects with randomized designs, textures, lighting conditions, and realistic depths. We perform extensive experiments demonstrating that CenterArt outperforms existing methods in accuracy and robustness.