U6DA

official Pytorch implementation of paper 'Adversarial samples for deep monocular 6D object pose estimation'

U6DA-Linemod

The dataset can be download from Google Drive and Baidu Pan (code: jcfm)

After download and unzip, back up the original data first, then:

cp ape/* lm/test/000001/rgb/
cp benchvise/* lm/test/000002/rgb/
cp cam/* lm/test/000004/rgb/
cp can/* lm/test/000005/rgb/
cp cat/* lm/test/000006/rgb/
cp driller/* lm/test/000008/rgb/
cp duck/* lm/test/000009/rgb/
cp eggbox/* lm/test/000010/rgb/
cp glue/* lm/test/000011/rgb/
cp holepuncher/* lm/test/000012/rgb/
cp iron/* lm/test/000013/rgb/
cp lamp/* lm/test/000014/rgb/
cp phone/* lm/test/000015/rgb/

• Our codes coming soon!

Citation

if you find our work useful in your research, please consider citing:

@article{zhang2022adversarial,
  title={Adversarial samples for deep monocular 6D object pose estimation},
  author={Zhang, Jinlai and Li, Weiming and Liang, Shuang and Wang, Hao and Zhu, Jihong},
  journal={arXiv preprint arXiv:2203.00302},
  year={2022}
}