# DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning

arXiv: https://arxiv.org/abs/2410.21758 · HTML: https://mlsanity.com/paper/2410.21758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022)
- [Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery](https://mlsanity.com/paper/2406.06092/) (2024)
- [DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects](https://mlsanity.com/paper/2203.11274/) (2022)
- [SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation](https://mlsanity.com/paper/2309.08728/) (2023)
- [Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research](https://mlsanity.com/paper/2204.10297/) (2022)
- [PokeFlex: Towards a Real-World Dataset of Deformable Objects for Robotic Manipulation](https://mlsanity.com/paper/2409.17124/) (2024)

## Built on by (most cited first, 3 of 3)

- [Manipulating Elasto-Plastic Objects With 3D Occupancy and Learning-Based Predictive Control](https://mlsanity.com/paper/2505.16249/) (2025)
- [TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objects](https://mlsanity.com/paper/2603.17068/) (2026)
- [Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos](https://mlsanity.com/paper/2610.17591/) (2026)

## Abstract (arXiv)

This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics, using a pinching strategy with a two-parallel-finger gripper. In addition, we trained a neural network with the down-sampled 3D occupancy and action as input to model the dynamics of an elasto-plastic object. Our dataset and all CADs of the data collection system will be released soon on our website.