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3DStructurePoints

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- __init__.py
- bhcp_dataloader.py
- data_utils.py
- shapenet_seg_dataloader.py
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- - data_ico.jpg
  - paper_thumbnail.jpg
  - paper_thumbnail_ori.jpg
  - pipeline.jpg
  - teaser.jpg
- glab.css
- index.html
- evaluate_corres_accuracy.py
- evaluate_label_transfer_accuracy.py
- teaser.png
- __init__.py
- chamfer_distance.py
- pointnet2_structure_point_net.py
- - __init__.py
  - linalg_utils.py
  - pointnet2_modules.py
  - pointnet2_utils.py
- __init__.py
- setup.py
- test_shape_correspondence.py
- test_structure_points.py
- __init__.py
- train_structure_points.py
- train_structure_points_for_label_transfer.py
- __init__.py
- check_points_utils.py
- logutils.py
- mesh_utils.py
- point_cloud_utils.py
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/NolenChen/3DStructurePoints

Downloads the entire project code from GitHub to your computer.

cd 3DStructurePoints

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# Unsupervised Learning of Intrinsic Structural Representation Points

## Description
This repository contains the code for our cvpr 2020 paper: [Unsupervised Learning of Intrinsic Structural Representation Points](https://arxiv.org/abs/2003.01661)

<div align="center">
<img src="https://github.com/NolenChen/3DStructurePoints/blob/master/figs/teaser.png" width="70%" height="70%"><br><br>
</div>

## Environment setup

Current Code is tested on ubuntu16.04 with cuda9, python3.6, torch 1.1.0 and torchvision 0.3.0. 
We use a [pytorch version of pointnet++](https://github.com/erikwijmans/Pointnet2_PyTorch) in our pipeline.
```
pip install -r requirements.txt
cd pointnet2
python setup.py build_ext --inplace
```

## Dataset

The training and testing data for 3D semantic correspondence is provided by [LMVCNN](https://people.cs.umass.edu/~hbhuang/local_mvcnn/) and [bhcp](http://www.vovakim.com/projects/CorrsTmplt/doc_data.html) respectively, and you can download the preprocessed training data [here](https://pan.baidu.com/s/1AdKPCw-HlUCQPdWN-YSItg?pwd=dtip).
And [here](https://pan.baidu.com/s/1uji7snxiomNhdJiiTo-EdA?pwd=6zar)'s the script for preprocessing the testing data.

## Train

```
cd train
python train_structure_points.py -data_dir PATH_TO_TRAINING_DATA -num_structure_points 16 -category plane -log_dir PATH_TO_LOG
```
* -data_dir: path to the preprocessed training data
* -num_structure_points: number of structure points
* -category: category to train on
* -log_dir: path to log dir

The trained model will be saved in PATH_TO_LOG/checkpoints/model

## Test

```
cd test
python test_structure_points.py -data_dir ../demo_data/plane -model_fname PATH_TO_TRAINED_MODEL -num_structure_points 16 -output_dir OUTPUT_PATH
```
* -model_fname: path to trained model
* -data_dir: path to the testing data
* -output_dir: output path.
* -num_structure_points: number of structure points, should be the same with training stage

The structure point will be outputed in off format, corresponding structure points will have same colors

## Shape Correspondence
Run the following command to train the network to produce 512 structure points:
```
cd train
python train_structure_points.py -data_dir PATH_TO_TRAINING_DATA -num_structure_points 512 -category plane -log_dir PATH_TO_LOG
```
Run the following command to test the correspondence between two shapes:
```
cd test
python test_shape_correspondence.py -model_fname PATH_TO_TRAINED_MODEL -num_structure_points 512 -src_shape_fname ../demo_data/shape_corres/src_and_query_pts/plane_src_pts.off -query_pts_fname ../demo_data/shape_corres/src_and_query_pts/plane_query_pts.off  -tgt_shape_fname ../demo_data/shape_corres/tgt_pts/plane_tgt_pts_1.off -out_corres_pts_fname PATH_TO_OUTPUT
```

To evaluate the correspondence accuracy, you need to first obtain the bhcp benchmark and then do the data preprocessing with the [script](https://pan.baidu.com/s/1uji7snxiomNhdJiiTo-EdA?pwd=6zar).
Then run the following command to evaluate:
```
cd evaluate
python evaluate_corres_accuracy.py -model_fname PATH_TO_TRAINED_MODEL -category chair -num_structure_points 512 -data_dir PATH_TO_THE_EVALUATION_DATA
```

The pretrained models can be found [here](https://pan.baidu.com/s/1Dw0dox1jdnuWYITcr_qUNw?pwd=ua3t)

## Label Transfer
Download the preprocessed data, pretrained models and training logs [here](https://pan.baidu.com/s/1PqFg-XyiMaoHDe7ajaNAxg?pwd=it1q)

For training, run the following command:
```
cd train
python train_structure_points_for_la bel_transfer.py 
```
Run the following command to evaluate:
```
cd evaluate
python evaluate_label_transfer_accuracy.py
```

## Citation
Please cite our paper if you find it useful in your research:

```
@inproceedings{chen2020unsupervised,
  title={Unsupervised Learning of Intrinsic Structural Representation Points},
  author={Chen, Nenglun and Liu, Lingjie and Cui, Zhiming and Chen, Runnan and Ceylan, Duygu and Tu, Changhe and Wang, Wenping},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9121--9130},
  year={2020}
}
```

## Contact
If you have any questions, please contact chennenglun@gmail.com