M. Ji, J. Gall, H. Zheng, Y. Liu, and L. Fang. SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis. ICCV, 2017
The poster pdf is also available.
- install Nvidia driver 375 + cuda 8.0 + cudnn v5.1
- install the conda environment by:
bash installEnv.sh - download the network model to the folder "./inputs/SurfaceNet_models" from the Dropbox folder
- if the conda environment has been installed, one can activate it by:
. activate SurfaceNet; deactivate it by:. deactivate. - in terminal run:
python main.py
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.
SurfaceNet is released under the MIT License (refer to the LICENSE file for details).
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 Conference on Computer Vision and Pattern Recognition},
pages={2307--2315},
year={2017}
}