This is the repository for the paper "Accurate Point Cloud Registration with Robust Optimal Transport".

The repository provides a general framework for point cloud/mesh registration, supporting both optimization- and learning-based registration approaches. It also provides a general framework for deep prediction tasks, e.g. for 3D landmark detection.

Besides, the repository to https://github.com/uncbiag/shapmagn contains our latest development code.

We release a large lung vessel tree dataset PVT1010. For usage, please refer to PVT1010_readme.md.

The PVT1010 dataset includes 1,010 pairs of high resolution inhale/exhale lung vascular trees extracted from 3D computed tomography (CT) images.

10 cases corresponding to the public DirLab-COPD Gene dataset which includes, for each pair, 300 expert annotated landmarks that are in correspondence with each other. These 10 cases are used as test cases.

1. Spline registration (supports anisotropic kernel)
2. Fluid registration (supports LDDMM)
3. Coherent Point Drift
4. Robust Optimal Transport (RobOT)
5. RobOT Projection (rigid, spline, LDDMM)
6. RobOT-based uniform mass sampling
7. Third party approaches (Probreg, Open3d, Teaser++)

1. Spline registration (supports anisotropic kernel)
2. Fluid registration (supports LDDMM)
3. Point cloud feature learning (self-supervised)
4. End-to-end registration (prealign, spline/LDDMM, postprocessing)

1. Landmark Detection

For detailed instructions refer to INSTALL.md.

Set ROBOT_PATH in global_variable.py to local robot path.

We provide a series of demos, which can be found in the directory robot/demos Here are two examples demonstrating how to run the optimization-based demos :

cd robot/robot/demos
python 2d_toy_reg.py
python ot_fun.py
python toy_reg.py
python partial_prealign_reg.py
python lung_gradient_flow.py
python lung_reg.py
python flyingkitti_reg.py
python ot_sampling.py

Here is a toy example on training deep feature learning on 10 dirlab pairs:

python run_task.py -ds ./demos/data/lung_data/lung_dataset_splits -o ./demos/output/training_feature_learning_on_one_case -tn deepfeature_pointconv_train -ts ./demos/settings/lung/deep_feature_training -g 0

Here is an example on robust optimal transport based deep feature projection (spline) on 10 dirlab pairs:

python run_eval.py -dj ./demos/data/lung_data/lung_dataset_splits/test/pair_data.json -o ./demos/output/test_feature_projection_one_case/deepfeature_pointconv_projection -ts ./demos/settings/lung/deep_feature_projection -g 0

Here is a toy example on training a pretrained deep LDDMM flow network on 10 dirlab pairs:

python run_task.py  -ds ./demos/data/lung_data/lung_dataset_splits -o ./demos/output/train_deepflow_on_one_case -tn deepflow_pwc_lddmm -ts ./demos/settings/lung/deep_lddmm_flow   -g 0

Here is a example on evaluating a deep LDDMM flow network on 10 dirlab pairs:

python run_eval.py -dj ./demos/data/lung_data/lung_dataset_splits/test/pair_data.json -o ./demos/output/test_deepflow_on_one_case/deepflow_pwc_lddmm -ts ./demos/settings/lung/deep_lddmm_flow  -m   ./demos/pretrained_models/lung_model/pretrained_deep_lddmm -g 0

Deep demo on Kitti

Preparing the Kitti data

We need to prepare the data of KITTI Scene Flow 2015 first. Here we follow the instructions in PointPWC.

Download and unzip KITTI Scene Flow Evaluation 2015 to directory RAW_DATA_PATH. Run the following script for KITTI data preprocessing; the processed data will be saved in PROCESSED_DATA_PATH; a robot compatible format is saved in SHAPMAN_INTPUT_PATH:

cd robot/experiments/datasets/flying3d_and_kitti/flyingkitti_nonocc
python process_kitti_raw_data.py RAW_DATA_PATH PROCESSED_DATA_PATH
python prepare_data_test_on_kitti.py RAW_DATA_PATH PROCESSED_DATA_PATH SHAPMAN_INTPUT_PATH

Here is an example on evaluating a spline flow network on 142 Kitti pairs:

python run_eval.py -dj SHAPMAN_INTPUT_PATH/pair_data.json -o ./demos/output/test_deepflow_on_kitti/deep_spline -ts ./demos/settings/kitti/deep_spline_flow  -m ./demos/pretrained_models/kitti_model/pretrained_deep_spline -g 0

Here is an example on evaluating a displacement network (PWC) that combines OT prealignement and OT postprocessing on 142 Kitti pairs:

python run_eval.py -dj SHAPMAN_INTPUT_PATH/pair_data.json -o ./demos/output/test_deepflow_on_kitti/deep_pwc -ts ./demos/settings/kitti/official_released_pwc_model_with_ot_prealigned_and_post  -m ./demos/pretrained_models/kitti_model/PointConv_726_0.0463.pth -g 0

Gallery

Here are some examples on supported functions:

Robust Optimal Transport (RobOT):

RobOT Projection (Partial Rigid Registration):

RobOT Projection (Spline, LDDMM):

Lung vessel Registration (60,000 points):

Scene Flow Estimation:

Self-supervised Feature Learning (60,000 points):

Synthesized Data Pair Generation:

Facial Landmark Detection (160,000 points):