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---
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license: cc-by-4.0
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---
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# RealArt-6
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This is the official dataset collected for [RPMArt](https://r-pmart.github.io/) to test the sim-to-real transfer. It contains 6 articulated object instances, each captured from 20 camera views under 5 states in scenarios with and without background, as well as presence or absence of distractors.
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## Dataset Structure
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```
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without_table
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├── microwave
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│ ├── 0_without_chaos
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│ │ ├── xyzrgb_00.npz # microwave point cloud from 00 camera view under 0 state in scenario without background and without distractors
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│ │ ├── xyzrgb_01.npz # microwave point cloud from 01 camera view under 0 state in scenario without background and without distractors
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│ │ └── ...
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│ ├── 1_without_chaos
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│ ├── ...
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│ ├── 0_with_chaos
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│ │ ├── xyzrgb_00.npz # microwave point cloud from 00 camera view under 0 state in scenario without background and with distractors
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│ │ ├── xyzrgb_01.npz # microwave point cloud from 01 camera view under 0 state in scenario without background and with distractors
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│ │ └── ...
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│ ├── 1_with_chaos
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│ └── ...
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├── refrigerator
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└── ...
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with_table
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├── microwave
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│ ├── 0_without_chaos
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│ │ ├── xyzrgb_00.npz # microwave point cloud from 00 camera view under 0 state in scenario with background and without distractors
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│ │ ├── xyzrgb_01.npz # microwave point cloud from 01 camera view under 0 state in scenario with background and without distractors
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│ │ └── ...
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│ ├── 1_without_chaos
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│ ├── ...
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│ ├── 0_with_chaos
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│ │ ├── xyzrgb_00.npz # microwave point cloud from 00 camera view under 0 state in scenario with background and with distractors
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│ │ ├── xyzrgb_01.npz # microwave point cloud from 01 camera view under 0 state in scenario with background and with distractors
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│ │ └── ...
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│ ├── 1_with_chaos
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│ └── ...
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├── refrigerator
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└── ...
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```
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## Dataset Creation
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All data are collected by a wrist-mounted Intel RealSense L515 LiDAR camera on a 7-DOF Franka Emika robot arm. The details of collection process are presented in the [paper](https://arxiv.org/abs/2403.16023).
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## Dataset Load
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```python
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import numpy as np
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data = np.load("./with_table/microwave/0_without_chaos/xyzrgb_00.npz")
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xyz = data['point_cloud'].astype(np.float32) # (N, 3)
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rgb = data['rgb'].astype(np.float32) # (N, 3)
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art = data['joints'] # (J, 10)
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joint_origins = art[:, 0:3].astype(np.float32) # (J, 3)
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joint_directions = art[:, 3:6].astype(np.float32) # (J, 3)
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affordable_points = art[:, 6:9].astype(np.float32) # (J, 3)
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articulation_types = art[:, -1].astype(np.int64) # (J,)
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```
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## Dataset License
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This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. If you find it helpful, please consider citing our work:
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```
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@article{wang2024rpmart,
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title={RPMArt: Towards Robust Perception and Manipulation for Articulated Objects},
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author={Wang, Junbo and Liu, Wenhai and Yu, Qiaojun and You, Yang and Liu, Liu and Wang, Weiming and Lu, Cewu},
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journal={arXiv preprint arXiv:2403.16023},
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year={2024}
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}
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```
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