We follow the main idea from [1] expect using differentiable renderer instead of original pre-render step. By differentiable renderer, we can chain each step to make a global refinement.
You can refer to original paper for more details.
This project is based on PyTorch and pytorch3d.
conda create -n pytorch3d python=3.8
conda activate pytorch3d
pip install -r requirement.txt
Please download the YCBDataset from here, and modified the dataroot in 'settings/ycb.yaml:DATA_ROOT=YOUR DATA PATH'.
Run python tools/train.py to start a new train.
We obey the evaluation step of DenseFusion.
Run python test.py to generate the result, which can directly use for evaluation. The result will be saved under ./result, and has the same data structure with DenseFusion.
We also provide a pretrained model, you can download them from (links: https://pan.baidu.com/s/1Wz_3A5fzDbT8Phc1QnGobw passwd: igh7).
[1] Li Y, Wang G, Ji X, et al. Deepim: Deep iterative matching for 6d pose estimation[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 683-698.