This repository implements the methods described in VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement. The code and data required to reproduce the results in Tables I, II and III are provided.

The code has been tested on Ubuntu 16.04 and 20.04 with Python 3.6. To set-up the Python environment, use Anaconda and the provided YAML file:

conda env create -f environment.yml --name verefine

conda activate verefine

GLFW is required for rendering:

sudo apt install libglfw3

We provide bindings for ICP and Trimmed ICP in PCL via pybind11. This requires the following dependencies to be installed:

sudo apt install build-essential libboost-dev libeigen3-dev libpcl-dev

pybind11 is installed through the conda environment. The bindings themselves can be built using:

bash src/refinement/cpp/build.sh

We use the BOP toolkit for evaluation. Please refer to this repository for installation instructions. We recommend to also install the CPP version of the renderer.

The LINEMOD and YCB VIDEO datasets can be downloaded on the BOP Challenge 2019 page. We require the base archive, object models and BOP test images. Extract them into some directory $BOP_DIR. In addition, we have converted the Rutgers Extended RGBD (Extended APC) dataset to the BOP format.

The converted dataset, pre-computed hypotheses pools for all evaluated baselines and datasets, collider meshes and the target definitions needed for evaluation are provided here. The following steps are required to prepare the datasets for the experiments and the evaluation:

• Extract the provided zip file into some directory $VEREFINE_DIR. There should be three directories - bop, bop_toolkit and verefine.
• The bop/lm/test_targets_verefine.json file needs to be moved to the respective BOP dataset folder in $BOP_DIR/lm.
• The bop/xapc directory needs to be moved to the BOP dataset directory $BOP_DIR.
• Copy the two files in bop_toolkit over the corresponding files in your BOP toolkit installation. This adds the definitions for the Extended APC dataset.
• Adapt the config file in src/verefine/config.py to reflect your dataset paths. PATH_BOP19 should point to the BOP dataset directory $BOP_DIR. PATH_VEREFINE should point to the additional files in $VEREFINE_DIR.

See evaluate.sh for the settings used to run the experiments. Note that the experiments for Tables I (ICP results) and II (TrICP) require the PCL bindings. Running src/util/experiment.py will create log files in the BOP format in the logs directory. The logs can be evaluated using the BOP toolkit.

For the LINEMOD and Extended APC datasets, use:

python $BOP_TOOLKIT_PATH/scripts/eval_bop19.py --result_filenames=$PATH_TO_LOG.csv --targets_filename=$BOP_DIR/$DATASET/test_targets_verefine.json

For the YCB VIDEO dataset, use:

python $BOP_TOOLKIT_PATH/scripts/eval_bop19.py --result_filenames=$PATH_TO_LOG.csv --targets_filename=$BOP_DIR/ycbv/test_targets_bop19.json