ryany1994/pvnet

Code for "PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation" CVPR 2019 oral

PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation

PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, Hujun Bao
CVPR 2019 oral
Project Page

Any questions or discussions are welcomed!

Truncation LINEMOD Dataset

Check TRUNCATION_LINEMOD.md for information about the Truncation LINEMOD dataset.

Installation

1. Set up python 3.6.7 environment

pip install -r requirements.txt

We need compile several files, which works fine with pytorch v0.4.1/v1.0.1 and gcc 5.4.0.

For users with a RTX GPU, you must use CUDA10 and pytorch v1.0.1 built from CUDA10.

2. Compile the Ransac Voting Layer

ROOT=/path/to/pvnet
cd $ROOT/lib/ransac_voting_gpu_layer
python setup.py build_ext --inplace

3. Compile some extension utils

cd $ROOT/lib/utils/extend_utils

Revise the cuda_include and dart in build_extend_utils_cffi.py to be compatible with the CUDA in your computer.

sudo apt-get install libgoogle-glog-dev=0.3.4-0.1
sudo apt-get install libsuitesparse-dev=1:4.4.6-1
sudo apt-get install libatlas-base-dev=3.10.2-9
python build_extend_utils_cffi.py

Add the lib under extend_utils to the LD_LIBRARY_PATH

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/pvnet/lib/utils/extend_utils/lib

Dataset Configuration

Prepare the dataset

Download the LINEMOD, which can be found at here.

Download the LINEMOD_ORIG, which can be found at here.

Download the OCCLUSION_LINEMOD, which can be found at here.

Create the soft link

mkdir $ROOT/data
ln -s path/to/LINEMOD $ROOT/data/LINEMOD
ln -s path/to/LINEMOD_ORIG $ROOT/data/LINEMOD_ORIG
ln -s path/to/OCCLUSION_LINEMOD $ROOT/data/OCCLUSION_LINEMOD

Compute FPS keypoints

python lib/utils/data_utils.py

Synthesize images for each object

See pvnet-rendering for information about the image synthesis.

Demo

Download the pretrained model of cat from here and put it to $ROOT/data/model/cat_demo/199.pth.

Run the demo

python tools/demo.py

If setup correctly, the output will look like

Visualization of the voting procedure

We add a jupyter notebook visualization.ipynb for the keypoint detection pipeline of PVNet, aiming to make it easier for readers to understand our paper. Thanks for Kudlur, M 's suggestion.

Training and testing

Training on the LINEMOD

Before training, remember to add the lib under extend_utils to the LD_LIDBRARY_PATH

export LD_LIDBRARY_PATH=$LD_LIDBRARY_PATH:/path/to/bb8-voter/lib/utils/extend_utils/lib

Training

python tools/train_linemod.py --cfg_file configs/linemod_train.json --linemod_cls cat

Testing

We provide the pretrained models of each object, which can be found at here.

Download the pretrained model and move it to $ROOT/data/model/{cls}_linemod_train/199.pth. For instance

mkdir $ROOT/data/model
mv ape_199.pth $ROOT/data/model/ape_linemod_train/199.pth

Testing

python tools/train_linemod.py --cfg_file configs/linemod_train.json --linemod_cls cat --test_model

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{peng2019pvnet,
  title={PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation},
  author={Peng, Sida and Liu, Yuan and Huang, Qixing and Zhou, Xiaowei and Bao, Hujun},
  booktitle={CVPR},
  year={2019}
}