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