---
license: mit
language:
- en
tags:
- robotics
- robotic-grasping
- target-driven-grasping
- occlusion
- 6dof-grasping
- 3d-vision
- shape-completion
- pytorch
library_name: pytorch
---

# TARGO-Net

This repository hosts the released checkpoints for **TARGO-Net**, the model from:

**TARGO and TARGO-Net: Benchmarking Target-Driven Object Grasping Under Occlusions**  
Accepted at **International Journal of Computer Vision (IJCV), 2026**.

- Project page: https://targo-benchmark.github.io/
- Paper DOI: https://doi.org/10.1007/s11263-025-02716-9
- arXiv: https://arxiv.org/abs/2407.06168

## Overview

TARGO is a benchmark for target-driven 6D robotic grasping under occlusion. It evaluates how grasping performance changes as target visibility decreases, using large-scale synthetic data and real-world scenes. TARGO-Net is a transformer-based grasping model with a shape completion module, designed to remain robust as occlusion increases.

## Checkpoints

| File | Description |
| --- | --- |
| `checkpoints/targonet.pt` | TARGO-Net grasp prediction checkpoint. |
| `checkpoints/adapointr.pth` | AdaPoinTr target shape completion checkpoint used by the TARGO-Net pipeline. |

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="randing2000/TARGO-Net",
    local_dir="checkpoints_hf",
    allow_patterns=["checkpoints/targonet.pt", "checkpoints/adapointr.pth"],
)
```

## Data

The benchmark/dataset files are not included in this model repository. Please see the project page for code, data, and benchmark details:

https://targo-benchmark.github.io/

## Citation

```bibtex
@article{xia2026targo,
  title={TARGO and TARGO-Net: Benchmarking Target-Driven Object Grasping Under Occlusions},
  author={Xia, Yan and Ding, Ran and Qin, Ziyuan and Zhan, Guanqi and Zhou, Kaichen and Yang, Long and Dong, Hao and Cremers, Daniel},
  journal={International Journal of Computer Vision},
  year={2026},
  doi={10.1007/s11263-025-02716-9}
}
```

## License

The model repository is released under the MIT license. Please also check the licenses of the benchmark data and any third-party assets used in your experiments.