PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Created by Yu Xiang at RSE-Lab at University of Washington and NVIDIA Research.

Introduction

We introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. arXiv, Project

License

PoseCNN is released under the MIT License (refer to the LICENSE file for details).

Citation

If you find PoseCNN useful in your research, please consider citing:

@inproceedings{xiang2018posecnn,
    Author = {Xiang, Yu and Schmidt, Tanner and Narayanan, Venkatraman and Fox, Dieter},
    Title = {PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes},
    Journal   = {Robotics: Science and Systems (RSS)},
    Year = {2018}
}

Installation

Install TensorFlow. I usually compile the source code of tensorflow locally.
Compile the new layers under $ROOT/lib we introduce in PoseCNN.
```
cd $ROOT/lib
sh make.sh
```
Download the VGG16 weights from here (528M). Put the weight file vgg16.npy to $ROOT/data/imagenet_models.
Compile lib/synthesize with cmake (optional). This package contains a few useful tools such as generating synthetic images for training and ICP.

Install dependencies:
- Pangolin
- Eigen
- boost
- Sophus
- nanoflann
- libsuitesparse-dev
We use Boost.Python library to link tensorflow with the c++ code. Make sure you have it in your Boost. The tested Boost version is 1.66.0.

Change hard coded pathes in CMakeLists.txt.

The Pangolin branch I use: c2a6ef524401945b493f14f8b5b8aa76cc7d71a9
```
cd $ROOT/lib/synthesize
mkdir build
cd build
cmake ..
make
```
Add the path of the built libary libsynthesizer.so to python path
```
export PYTHONPATH=$PYTHONPATH:$ROOT/lib/synthesize/build
```

Required environment

Ubuntu 16.04
Tensorflow >= 1.2.0
CUDA >= 8.0

Running the demo

Download our trained model on the YCB-Video dataset from here, and save it to $ROOT/data/demo_models.
run the following script
```
./experiments/scripts/demo.sh $GPU_ID
```

Running on the YCB-Video dataset

Download the YCB-Video dataset from here.
Create a symlink for the YCB-Video dataset (the name LOV is due to legacy, Learning Objects from Videos)
```
cd $ROOT/data/LOV
ln -s $ycb_data data
ln -s $ycb_models models
```

Training and testing on the YCB-Video dataset

cd $ROOT

# training
./experiments/scripts/lov_color_2d_train.sh $GPU_ID

# testing
./experiments/scripts/lov_color_2d_test.sh $GPU_ID

yarrseni/PoseCNN