This is a cleaner version of COPE, no shared data loader and evaluation; camera instrinsics are passed as input to the network, allowing different intrinsics during training and testing. Repository for generating the paper results is available at https://github.com/sThalham/COPE/tree/cope_WACV
Stefan Thalhammer,
Timothy Patten,
Markus Vincze,
Accepted for publication at WACV: Winter Conference on Applications in Computer Vision, 2023, algorithms track
[Paper]
Please cite the paper if you are using the code:
@inproceedings{thalhammer2023cope,
title= {COPE: End-to-end trainable constant runtime object pose estimation}
author={S. {Thalhammer} and T. {Patten} and M. {Vincze}},
journal={arXiv preprint arXiv:2208.08807},
year={2022}}
git clone https://github.com/sThalham/COPE.git
python3 -m pip install opencv-python==4.4.0.40
python3 -m pip install pillow
python3 -m pip install matplotlib
python3 -m pip install transforms3d
python3 -m pip install glumpy
python3 -m pip install open3d-python
python3 -m pip install PyOpenGL
python3 -m pip install imgaug
Alternatively, use the provided Dockerfile to deploy a Docker container that satisfies the version requirements.
Notes:
- Results in the paper are generated using NVIDIA CUDA 11.6