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An Unofficial Pytorch Implementation of MVSNet

MVSNet: Depth Inference for Unstructured Multi-view Stereo. Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, Long Quan. ECCV 2018. MVSNet is a deep learning architecture for depth map inference from unstructured multi-view images.

This is an unofficial Pytorch implementation of MVSNet

Download the preprocessed DTU training data (Fixed training cameras, from Original MVSNet), and upzip it as the MVS_TRANING folder
in train.sh, set MVS_TRAINING as your training data path
create a logdir called checkpoints
Train MVSNet: ./train.sh

Download the preprocessed test data DTU testing data (from Original MVSNet) and unzip it as the DTU_TESTING folder, which should contain one cams folder, one images folder and one pair.txt file.
in test.sh, set DTU_TESTING as your testing data path and CKPT_FILE as your checkpoint file. You can also download my pretrained model.
Test MVSNet: ./test.sh

in eval.py, I implemented a simple version of depth map fusion. Welcome contributions to improve the code.

	Acc.	Comp.	Overall.
MVSNet(D=256)	0.396	0.527	0.462
PyTorch-MVSNet(D=192)	0.4492	0.3796	0.4144

Due to the memory limit, we only train the model with D=192, the fusion code is also different from the original repo.