https://github.com/mjiUST/SurfaceNet

2017 ICCV, SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
https://github.com/mjiUST/SurfaceNet

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2017 ICCV, SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis

• Host: GitHub
• URL: https://github.com/mjiUST/SurfaceNet
• Owner: mjiUST
• License: other
• Created: 2017-08-04T01:25:53.000Z (about 7 years ago)
• Default Branch: github.SurfaceNet
• Last Pushed: 2020-07-14T07:04:53.000Z (about 4 years ago)
• Last Synced: 2024-04-18T18:55:58.934Z (5 months ago)
• Language: Python
• Homepage:
• Size: 9.29 MB
• Stars: 122
• Watchers: 9
• Forks: 36
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-computer-vision-papers - 2017ICCV

README

        
# News

- [SurfaceNet+](https://github.com/mjiUST/SurfaceNet-plus) is available in 2020 TPAMI!

# SurfaceNet

M. Ji, J. Gall, H. Zheng, Y. Liu, and L. Fang. [SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis](https://www.researchgate.net/publication/318920947_SurfaceNet_An_End-to-end_3D_Neural_Network_for_Multiview_Stereopsis). ICCV, 2017

The [poster pdf](https://www.researchgate.net/publication/321126305_ICCV2017_SurfaceNet_poster) is also available.

![SurfaceNet experiment results](figures/experiment.png?raw=true "SurfaceNet experiment results")

![SurfaceNet pipeline](figures/pipeline.png?raw=true "SurfaceNet pipeline")

## How to run

1. install [Nvidia driver 375 + cuda 8.0 + cudnn v5.1](https://github.com/mjiUST/driver_cuda_cudnn)

2. install the conda environment by: `bash installEnv.sh`

    * DON'T WORRY, conda will generate an isolated environment for SurfaceNet with python2.7, anaconda, theano, ... etc. That means all your libraries / packeges' version will not be affacted, at the same time the `~/.bashrc` file will not be changed.

    * before you run, PLEASE change the CUDA/CUDNN path in the files: 

        - `./config/activate-cuda.sh` change the 1st line to your cuda path, e.g.: `export CUDA_ROOT=/usr/local/cuda`

        - `./config/activate-cudnn.sh` change the 1st line to your cudnn path, e.g.: `export CUDNN_ROOT=/home//libs/cudnn`

3. download the network model to the folder "./inputs/SurfaceNet_models" from the Dropbox [folder](https://www.dropbox.com/sh/8xs0u57ikj4qfvr/AADRQFQyJfG3WfH7ZvpcWmMKa?dl=0)

4. if the conda environment has been installed, one can activate it by: `. activate SurfaceNet`; deactivate it by: `. deactivate`.

5. in terminal run: `python main.py` 

## Evaluation results

Some evaluation results are uploaded, including '.ply' files and the detailed number of Table 3. This could be helpful if you want to compare with this work.

## License

SurfaceNet is released under the MIT License (refer to the LICENSE file for details).

## Citing SurfaceNet

If you find SurfaceNet useful in your research, please consider citing:

    @inproceedings{ji2017surfacenet,

      title={SurfaceNet: An End-To-End 3D Neural Network for Multiview Stereopsis},

      author={Ji, Mengqi and Gall, Juergen and Zheng, Haitian and Liu, Yebin and Fang, Lu},

      booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},

      pages={2307--2315},

      year={2017}

    }