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
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
@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.