DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning
This provides a dataset for researchers in robotics and computer vision to study deformable object manipulation, though it is incremental as it focuses on data collection rather than novel methods.
The authors introduced DOFS, a real-world 3D dataset of deformable objects with full spatial information, collected using a low-cost platform, and trained a neural network to model object dynamics.
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.