| --- |
| 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. |
|
|
