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Shapmagn

This is a temporary repository, containing our latest development code, for the paper "Accurate Point Cloud Registration with Robust Optimal Transport". The official released one (with stable features and easy installation) can be found at here: https://github.com/uncbiag/robot

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.

PVT1010 Dataset

We release a large lung veseel 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.

Supported approaches:

Optimization-based approaches:

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++)

Deep learning-based registration approaches:

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)

Other deep learning-based approaches:

1. Landmark Detection

Installation

For detailed instructions refer to INSTALL.md.

Set SHAPMAGN_PATH in global_variable.py to local shapmagn path.

Demo

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

Optimization demos

cd shapmagn/shapmagn/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

Deep demo on Lung vessel tree

Here is a <em>toy<em> 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 <em>toy<em> 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 shapmagn compatible format is saved in SHAPMAN_INTPUT_PATH:

cd shapmagn/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):