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

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

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

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

@inproceedings{xiang2017posecnn,
    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   = {arXiv preprint arXiv:1711.00199},
    Year = {2017}
}

1. Install TensorFlow. I usually compile the source code of tensorflow locally.

2. Download the VGG16 weights from here (528M). Put the weight file vgg16.npy to $ROOT/data/imagenet_models.

3. Compile lib/synthesize with cmake. This package contains a few useful tools such as generating synthetic image and ICP.

   Install dependencies:

   • Pangolin
   • Eigen
   • Sophus
   • nanoflann
   • libsuitesparse-dev

   cd $ROOT/lib/synthesize
   mkdir build
   cd build
   cmake ..
   make

   Compile the Cython interface for lib/synthesize and custom layers

   cd $ROOT/lib
   python setup.py build_ext --inplace

   Add the libary path

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$ROOT/lib/synthesize/build

• Ubuntu 16.04
• Tensorflow >= 1.2.0
• CUDA >= 8.0

1. Download our trained model on the YCB-Video dataset from here, and save it to $ROOT/data/demo_models.

2. run the following script

   ./experiments/scripts/demo.sh $GPU_ID

1. Download the YCB-Video dataset from here.

2. 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

3. 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