#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/partscan" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for PartScan

![image/png](https://huggingface.co/datasets/Voxel51/partscan/resolve/main/partscan.gif)

![FiftyOne](https://img.shields.io/badge/FiftyOne-3D%20point%20cloud-orange)

Fine-grained 3D part segmentation is crucial for embodied AI systems that must
interact with specific functional components of an object (e.g. a *drawer handle*
rather than the whole *cabinet*). Acquiring dense, part-level 3D annotations is a
major bottleneck, so PinPoint3D introduces a 3D data-synthesis pipeline that
produces a large-scale, **scene-level** dataset with dense part annotations on
sparse, real-world-style scans.

**partscan** has been parsed as a FiftyOne **3D point cloud** dataset of scene-level scans with
dense, per-point **part-level** annotations. It is the synthesized dataset
introduced for **PinPoint3D**, a framework for fine-grained, multi-granularity 3D
part segmentation from a few user clicks. Each sample is a colored point cloud of
one scene fragment, rendered in the FiftyOne App’s 3D viewer.

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from huggingface_hub import snapshot_download


# Download the dataset snapshot to the current working directory

snapshot_download(
    repo_id="Voxel51/partscan", 
    local_dir=".", 
    repo_type="dataset"
    )

# Load dataset from current directory using FiftyOne's native format
dataset = fo.Dataset.from_dir(
    dataset_dir=".",  # Current directory contains the dataset files
    dataset_type=fo.types.FiftyOneDataset,  # Specify FiftyOne dataset format
    name="PartScan"  # Assign a name to the dataset for identification
)

# Launch the App
session = fo.launch_app(dataset)

```

## Dataset Details

### Dataset Description

This FiftyOne dataset wraps each scan as an `.fo3d` point-cloud scene
(`fo3d.PlyMesh`, `is_point_cloud=True`), so the per-point RGB color is rendered
directly in the App’s interactive 3D viewer. Scene/fragment identifiers follow
the ScanNet-style `sceneXXXX_YY` naming convention.

- **Paper:** [PinPoint3D: Fine-Grained 3D Part Segmentation from a Few Clicks](https://arxiv.org/abs/2509.25970) (Zhang et al., SUSTech)
- **Repo:** https://github.com/Quit123/PinPoint3D
- **Project Page:** https://pinpoint3d.online/

---

## FiftyOne Dataset Structure

**Dataset name:** `partscan`

**Media type:** `3d`

### Summary

| Property                 | Value   |
|--------------------------|---------|
| Samples (scan fragments) | 1,509   |
| Unique scenes            | 707     |
| Fragments per scene      | 1–7     |

### Per-point data (in each PLY)

Each point carries `x, y, z` coordinates, `red, green, blue` color, and a
`label` part ID. A label of `-1` denotes an unlabeled / ignore point.

### Sample-level fields

| Field                | Type      | Description                                    |
|----------------------|-----------|------------------------------------------------|
| `scene_id`           | string    | Scene identifier, e.g. `scene0002`             |
| `fragment`           | string    | Fragment suffix within the scene, e.g. `01`    |
| `num_points`         | int       | Total number of points in the scan             |
| `unique_labels`      | list(int) | Distinct part labels present (excluding `-1`)  |
| `num_labeled_points` | int       | Number of points with a valid (`!= -1`) label  |
| `ignore_fraction`    | float     | Fraction of points with label `-1` (unlabeled) |

## Citation

```bibtex
@article{zhang2025pinpoint3d,
  title   = {PinPoint3D: Fine-Grained 3D Part Segmentation from a Few Clicks},
  author  = {Zhang, Bojun and Ye, Hangjian and Zheng, Hao and Huang, Jianzheng and Lin, Zhengyu and Guo, Zhenhong and Zheng, Feng},
  journal = {arXiv preprint arXiv:2509.25970},
  year    = {2025}
}
```

## License

Please refer to the PinPoint3D project for the source dataset’s licensing terms.