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CVP-MVSNet (CVPR 2020 Oral) is a cost volume pyramid based depth inference framework for Multi-View Stereo.

CVP-MVSNet is compact, lightweight, fast in runtime and can handle high resolution images to obtain high quality depth map for 3D reconstruction.

If you find this project useful for your research, please cite:

@inproceedings{yang2020cvpmvsnet,
    title={Cost Volume Pyramid Based Depth Inference for Multi-View Stereo},
    author={Jiayu Yang, Wei Mao, Jose M. Alvarez and Miaomiao Liu},
    year={2020},
    booktitle={CVPR},
}

• Nvidia GPU with 11GB or more vRam.
• CUDA 10.1
• python3.6
• python2.7 for fusion script

git clone https://github.com/JiayuYANG/CVP-MVSNet.git

Testing data(2G):

Download our pre-processed DTU testing data from here and extract it to CVP-MVSNet/datasets/dtu-test-1200.

cd CVP_MVSNet

pip3 install -r requirements.txt

sh eval.sh

When finished, you can find depth maps in output_pretrained folder.

Check out Yao Yao's modified version of fusibile

git clone https://github.com/YoYo000/fusibile

Install fusibile by cmake . and make, which will generate the executable atFUSIBILE_EXE_PATH

Link fusibile executable into fusion folder

ln -s FUSIBILE_EXE_PATH CVP_MVSNet/fusion/fusible

Install extra dependencies

pip2 install -r CVP_MVSNet/fusion/requirements_fusion.txt

Use provided script to use fusibile to generate point clouds.

cd CVP_MVSNet/fusion/

sh fusion.sh

Use provided script to move generated point clouds into outputs_pretrained/dtu_eval folder

python fusibile_to_dtu_eval.py

Evaluate the point clouds using the DTU evaluation code.

The results should be like:

Download training dataset from here and extract it to CVP-MVSNet/datasets/dtu-train-128.

Modify training parameters in train.sh script.

Start training

sh train.sh

This repository is partly based on the MVSNet_pytorch repository by Xiaoyang Guo. Many thanks to Xiaoyang Guo for the great code!

This repository is inspired by the MVSNet by Yao Yao et al. Many thanks to Yao Yao and his mates for the great paper and great code!