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SAGNet: Structure-aware Generative Network for 3D-Shape Modeling

by Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, Hui Huang

Project page

The project page is available at https://vcc.tech/research/2019/SAGnet/.

Introduction

This repository contains an official implementation for SAGNet: Structure-aware Generative Network for 3D-Shape Modeling. This model takes voxel maps as geometry and bounding boxes as structure and learn a latent space through a joint analysis. The network is implemented with Python and TensorFlow.

To train the framework, you need CUDA-compatible GPUs. However, if you just want to generate 3D shapes, you may load the pretrained model trained on our own training data and follow the Test Model section. In addition, codes to visualize the synthesized results are also included.

If you have questions, please feel free to contact Zhijie Wu (wzj.micker@gmail.com).

overview

Starting

The current released codes are tested on Ubuntu 16.04. To train this network properly, please install the follow dependencies:

The PyQt5, qdarkstyle and VTK packages are required for running the visualization application. All these python packasges can be easily found in the common package list of Python distributions. Note that, maybe the VTK, PyQt have to be 5.10.1 and 5.6.0 for better package compatibility. We trained our model with tensorflow 1.2.0, but more advanced version should also work.

Then you can do something to clone our repository.

git clone git@github.com:zhijieW-94/SAGNet.git

Get training data

In this project, we segment some 3D shapes collected from 3D ShapeNetCore by ourselves. There are five classes of objects, including airplane, chair, guitar, lamp and motorbike. To make our life easier, we compute corresponding voxel maps and bounding boxes for these 3D shapes as training data.

And we build a directory for each class of them. To receive a quick start, we should first unzip the file (training_data.zip) to /data and then we can see five directories.

cd data
unzip training_data.zip

Train Model

In order to train a model to synthesize 3D shapes, we should set some essential information in the TRAIN section of config.yml.

TRAIN:
  BATCH_SIZE: 10  # mini batch size
  GPU_ID: [0,1,2,3] # the gpus for training
  ...
  ITER_NUM: 80000
  ...
  DIR_PATH: 'training_data/'
  RESULTS_DIRECTORY: 'nn_exp_results/reconstruction_results/'
  LOG_DIRECTORY: 'nn_exp_results/logs/'
  MODEL_DIRECTORY: 'nn_exp_results/models/'
  
  PRETRAINED_MODEL_PATH: 'trained_models/weights_17900.ckpt'
 
  SHAPE_NAME: 'chair'

Generally, the config.yml is written in json format and contain the following options.

After the config.yml has been set well, then we directly run train_net.py to start model training as:

python train_net.py

During training, we can monitor if the model is trained well or not with the help of log file and the reconstruction results. When the training goes well, we can see a directory named shapename_timestamp will be created in RESULTS_DIRECTORY, MODEL_DIRECTORY and LOG_DIRECTORY respectively. The frequencis are determined by SNAPSHOT_FREQ and SUMMARY_FREQ options.

Test Model

Before synthesizing 3D shapes with the trained model, we should set the config.yml as:

TEST:
  GPU_ID: [7] # the gpus for testing

  RESULTS_DIRECTORY: 'nn_exp_results/testing_results/'
  PRETRAINED_MODEL_PATH: 'trained_models/weights_67999.ckpt'

  SAMPLE_SIZE: 10

In the TEST section, this file (config.yml) has the following options:

If everything is OK, a model_info.txt will be included in the directory that stores corresponding pretrained model. And the model info file is used to indicate which class the generated shapes belong to and some other essential information for shape synthesis. Then you can directly run the test_net.py to synthesize SAMPLE_SIZE shapes as:

python test_net.py

Finally, the model will create SAMPLE_SIZE 3D shapes in RESULTS_DIRECTORY and each shape corresponds to a sub-directory named by a number. In each sub-directory, there are several .mat and .txt files to represent the voxel maps and bounding boxes.

Visualize Results

We also provide a tool to visualize the training data or the generated shapes. Note that, both the training data and results are composed of voxel maps and bounding boxes. You can run the visualization application as:

cd Visualization
python application.py

With the provided tool, you can visualize a group of results or a single shape. For example, to visualize all the motorbike training data, you should click the Open button and choose the data/motorbike directory (by default). And when you aim to visualize a single motor shape (e.g. named 1d8fb258aac4175a5834749a5ef37100), just click Openone button and choose the data/motorbike/1d8fb258aac4175a5834749a5ef37100 directory.

Note: If you have problems with the vtk or PyQt when run the visualization codes, please download our Anaconda environment and then clone it. Here is the link: https://drive.google.com/file/d/1W_Uy0W-2loC2V99tvPo6ZO4CEZbxbvg-/view?usp=sharing. You can solve most problems in this way.

Checkpoints

Checkpoint files of the final model trained over all five classes are provided. Please download them here and then paste the path of the unzipped checkpoint file (.cptk) to the PRETRAINED_MODEL_PATH option.

Cite

If you use our code/model/data, please cite our paper:

@article{SAGnet19,
title = {SAGNet: Structure-aware Generative Network for 3D-Shape Modeling},
author = {Zhijie Wu and Xiang Wang and Di Lin and Dani Lischinski and Daniel Cohen-Or and Hui Huang},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019)},
volume = {38},
number = {4},
pages = {91:1--91:14},  
year = {2019},
}

License

MIT License