构建首个含全空间信息的3D可变形物体数据集，助力物理动力学建模。
DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning
- 用透明操作平台和夹持策略采集多视角RGB-D与点云数据。
- 包含300+次动作、完整3D形变网格与带语义的体素占用信息。
- 适合机器人感知与可变形体物理建模研究者使用。
本文提出DOFS，首个包含完整空间信息（顶、侧、底）的3D可变形物体数据集，涵盖弹性-塑性物体。通过新型低成本透明操作平台，结合双平行手指夹持策略，采集了主动操作动作、多视角RGB-D图像、精确配准点云、3D形变网格及带语义的3D体素占用数据。数据集共包含300余次操作实验。基于下采样3D体素占用与动作输入，训练神经网络以学习弹性-塑性物体的动力学模型。所有数据及数据采集系统CAD文件将公开发布于官网。
原文摘要 · Abstract (English)
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.
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