### <a href="http://kangxue.org/papers/p2pnet.pdf"> P2P-NET: *Bidirectional Point Displacement Net for Shape Transform*</a>
<a href="http://kangxue.org">Kangxue Yin</a>, <a href="http://vcc.szu.edu.cn/~huihuang/">Hui Huang</a>, <a href="http://www.cs.tau.ac.il/~dcor/">Daniel Cohen-Or</a>, <a href="https://www.cs.sfu.ca/~haoz/">Hao Zhang</a>.

P2P-NET is the first general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc.
The architecture of the P2P-NET is that of a bi-directional point displacement network, which transforms a source point set to a target point set with the same cardinality, and vice versa, by applying point-wise displacement vectors learned from data. 
P2P-NET is trained on paired shapes from the source and target domains, but without relying on point-to-point correspondences between the source and target point sets...  [<a href="http://kangxue.org/papers/p2pnet.pdf">more in the paper</a>].


### Citation
If you find our work useful in your research, please consider citing:

    @article {yin2018p2pnet,
      author = {Kangxue Yin and Hui Huang and Daniel Cohen-Or and Hao Zhang},
      title = {P2P-NET: Bidirectional Point Displacement Net for Shape Transform},
      journal = {ACM Transactions on Graphics(Special Issue of SIGGRAPH)},
      volume = {37},
      number = {4},
      pages = {Article 152},
      year = {2018}
    }


![teaser](figures/interpolate.jpg)

Fig. 1. An example of application: 
P2P-NET learns geometric transformations between point sets, e.g., from cross-sectional profiles to 3D shapes, as shown. User can edit the profiles to create an interpolating sequence (top). Our network transforms all of them into point-based 3D shapes.  See more examples of results in the paper.
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### Prerequisites

- Linux (tested on Ubuntu 16.4 )
- Python (tested on 2.7)
- Tensorflow (tested on 1.3.0-GPU )
- h5py

The code is built on the top of 
<a href="https://github.com/charlesq34/pointnet2">PointNET++</a>.  Before run the code, please compile the   customized Tensorflow operators of PointNet++ under folder "pointnet_plusplus".

### Dataset

Download dataset compressed in HDF5: 
 <a href="https://www.dropbox.com/s/fz3khhwx6cxdnb5/data_hdf5.zip?dl=1">HERE</a>.


Download raw obj and ply files:
 here.



### Usage

A example of training P2P-NET 

(to learn transformations between point-based skeletons and point-based surfaces with dataset of airplanes)

	python -u run.py --mode=train  --train_hdf5='data_hdf5/airplane_train.hdf5'   --test_hdf5='data_hdf5/airplane_test.hdf5' --domain_A=skeleton --domain_B=surface  --gpu=0

Test the model:

	python -u run.py --mode=test  --train_hdf5='data_hdf5/airplane_train.hdf5'   --test_hdf5='data_hdf5/airplane_test.hdf5' --domain_A=skeleton --domain_B=surface  --gpu=0 --checkpoint='output_airplane_skeleton-surface/trained_models/epoch_200.ckpt'


### Acknowledgments
The code is built on the top of 
<a href="https://github.com/charlesq34/pointnet2">PointNET++</a>. 
Thanks for the precedent contribution.