PyTorch implementation of Paper "Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects" (ECCV 2022)
- [project page]
- [paper]
If you find the code useful, please consider citing:
@article{zhao2022fusing,
title={Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects},
author={Zhao, Chen and Hu, Yinlin and Salzmann, Mathieu},
journal={arXiv preprint arXiv:2203.08472},
year={2022}
}Our code has been tested with the the following dependencies: Python 3.7.11, Pytorch 1.7.1, Python-Blender 2.8, Pytorch3d 0.6.0, Python-OpenCV 3.4.2.17, Imutils 0.5.4. Please start by installing all the dependencies:
conda create -n UnseenObjectPose python=3.7.11
source activate UnseenObjectPose
conda install -c conda-forge imutils
conda install -c pytorch pytorch=1.7.1 torchvision
conda install pytorch3d -c pytorch3d
conda install -c jewfrocuban python-blender
pip install opencv-python
pip install tqdm
pip install imageio
pip install pyyaml
First please download the LineMOD dataset we used in our experiments from LineMOD. The data should be organized as
UnseenObjectPose
|-- data
|-- linemod_zhs
|-- models
|-- real
We generate 10,000 reference images for each object by rendering. The number of reference images and data path can be changed by modifying cfg["RENDER"]["NUM"] and cfg["RENDER"]["OUTPUT_PATH"], respectively. Please run the following code for rendering:
cd ./Render
python data_generation.py
The model can be trained on LineMOD by running:
python ./train.py
The unseen objects are 'APE', 'BENCHVISE', 'CAM', 'CAN' by default.
The pretrained models are also available at ./pretrained_models.