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[CVPR'17] Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
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benchmark train/test files Apr 11, 2017
evaluate evaluation code Apr 12, 2017
external flann Apr 12, 2017
imgs reduce size Apr 12, 2017
shapesynth shape synthesis Apr 12, 2017
torch
LICENSE.txt license Apr 20, 2018
README.md

README.md

cnncomplete

This repo contains code to train a volumetric deep neural network to complete partially scanned 3D shapes. More information can be found in our paper.

Data

Train/test data is available for download on our project website.

Code

Installation:

Training tasks use Torch7, with torch packages cudnn, cunn, torch-hdf5, xlua.

Matlab visualization of the isosurface in testing uses the matio package.

The shape synthesis code was developed under VS2013, and uses flann (included in external).

Training:

  • th train_class.lua -model epn-unet-class -save logs-epn-unet-class -train_data data/h5_shapenet_dim32_sdf/train_shape_voxel_data_list.txt -test_data data/h5_shapenet_dim32_sdf/test_shape_voxel_data_list.txt -gpu_index 0
  • For more options, see help: th train_class.lua -h or th train.lua -h
  • Trained models: trained_models.zip (700mb)

Testing:

  • th test.lua --model_path [path to model] --test_file sampledata/scan.h5 --output_path [path to output] --classifier_path [path to classifier model, only specify if using epn-class or epn-unet-class models]
  • For more options, see help: th test.lua -h

Citation:

@inproceedings{dai2017complete,
  title={Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis},
  author={Dai, Angela and Qi, Charles Ruizhongtai and Nie{\ss}ner, Matthias},
  booktitle = {Proc. Computer Vision and Pattern Recognition (CVPR), IEEE},
  year = {2017}
}

License

This code is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (please refer to LICENSE.txt for details).

Contact:

If you have any questions, please email Angela Dai at adai@cs.stanford.edu.

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