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---
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language:
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- en
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license: mit
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license_link: LICENSE
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pretty_name: omnicvproject
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tags:
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- 3d
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- timeseries
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task_categories:
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- robotics
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- depth-estimation
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dataset_info:
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features:
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- name: metadata
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dtype: string
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- name: left
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dtype: image
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- name: right
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dtype: image
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- name: depth
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dtype: image
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splits:
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- name: train
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num_bytes: 901589471
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num_examples: 100
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download_size: 900986407
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dataset_size: 901589471
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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# Omni Instrument CV Project Dataset
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The **Omni Instrument CV Project** is a compact robotics dataset combining:
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- [x] Raw ROS 2 MCAP bag (stereo, depth, TF, odom)
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- [x] Ground truth mesh (STL)
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- [x] Distilled synchronized dataset (100 samples)
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---
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# Contents
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## 1. **Raw Data**
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Contains a stripped ROS 2 MCAP recording:
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- `/zed/zedxm/left/color/rect/image`
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- `/zed/zedxm/right/color/rect/image`
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- `/zed/zedxm/depth/depth_registered`
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- `/zed/zedxm/odom`
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- `/tf`, `/tf_static`
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- Associated `CameraInfo`
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<p align="center">
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<img src="assets/neural_depth_demo.gif" width="1000" style="object-fit:fill;">
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</p>
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## 2. **Meshes**
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Includes:
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- [omni_mesh.stl](meshes/omni_mesh.stl) — ground-truth geometry for TSDF + reconstruction metrics.
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<p align="center">
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<img src="assets/ground_truth_mesh.png" width="1000" style="object-fit:fill;">
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</p>
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## 3. **HuggingFace Sample**
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Load a sample and decode its metadata:
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```python
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from datasets import load_dataset
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import json
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ds = load_dataset("OmniInstrument/CV_project")
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sample = ds[0]
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meta = json.loads(sample["metadata"])
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timestamp = meta["timestamp"]
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position = meta["position"] # [x, y, z]
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orientation = meta["orientation"] # [qx, qy, qz, qw]
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print("Timestamp:", timestamp)
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print("Position:", position)
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print("Orientation:", orientation)
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```
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## License
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This software and dataset are released under the [MIT License](LICENSE). |
