Sketch2CAD

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최종 버전 다운로드 (.zip)

- README.md
- trained_network.cpp
- trained_network.h
- teaser.png
- - __init__.py
  - build.sh
  - decode_block_op.cc
- - combine_whole_graph.py
  - loader.py
  - network.py
  - test_addSub_reg.py
  - test_bevel_reg.py
  - test_extrusion_reg.py
  - test_opt_cls.py
  - test_sweep_reg.py
  - train_addSub_reg.py
  - train_bevel_reg.py
  - train_extrusion_reg.py
  - train_opt_cls.py
  - train_sweep_reg.py
- - __init__.py
  - freeze_graph_tool.py
  - lmdb_to_tfrecords.py
  - util_funcs.py
- .gitignore
- README.md
.gitignore
LICENSE
README.md

// repository documentation

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# Sketch2CAD: Sequential CAD Modeling by Sketching in Context
![](https://raw.githubusercontent.com/Enigma-li/Sketch2CAD/master/docs/teaser.png)

## Introduction
This repository contains the implementation of [Sktech2CAD](http://geometry.cs.ucl.ac.uk/projects/2020/sketch2cad/) proposed in our SIGGRAPH Asia 2020 paper.
* **Sketch2CAD: Sequential CAD Modeling by Sketching in Context**<br/>
By [Changjian Li](https://enigma-li.github.io/), [Hao Pan](http://haopan.github.io/), [Adrien Bousseau](http://www-sop.inria.fr/members/Adrien.Bousseau/), [Niloy Mitra](http://www0.cs.ucl.ac.uk/staff/n.mitra/),<br/>
*ACM Trans. Graphics, 39(6), 2020, proceedings of SIGGRAPH Asia 2020.*

It contains two parts: 1) **network training**, 2) **training dataset** and **trained network deployment** (e.g., for interactive modeling).

The code is released under the MIT license.

### Network training

💡 ***Great News***: we have released the docker image for network training, which will greatly reduce the configuration burden, please check the *networkTraining* folder for more details.

This part contains the **Python** code for building, training and testing the nueral network using [TensorFlow](https://www.tensorflow.org/).

Please read README file within the *networkTraining* folder for more details.

### Training dataset and network deployment
This part contains the code for deploying the trained network in a C++ project that can be an interactive 3D modeling application. It also provides instructions to download the training dataset we generated, and our trained networks.

Please read the README file in *dataAndModel* folder for more details.

### Prototype System
We released the prototype system for research use, feel free to download and try it. More instructions to download it, please refer to the [project page](http://geometry.cs.ucl.ac.uk/projects/2020/sketch2cad/).

## Citation
If you use our code or model, please cite our paper:

@Article{Li:2020:Sketch2CAD, 
		Title = {Sketch2CAD: Sequential CAD Modeling by Sketching in Context}, 
	    	Author = {Changjian Li and Hao Pan and Adrien Bousseau and Niloy J. Mitra}, 
	    	Journal = {ACM Trans. Graph. (Proceedings of SIGGRAPH Asia 2020)}, 
	    	Year = {2020}, 
	    	Number = {6}, 
	    	Volume = {39},
	    	Pages = {164:1--164:14},
	    	numpages = {14},
	    	DOI = {https://doi.org/10.1145/3414685.3417807},
	    	Publisher = {ACM} 
 	}
 
 
## Contact
Any question you could contact Changjian Li (chjili2011@gmail.com) or Hao Pan (haopan@microsoft.com) for help.

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