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

How to Use

Environment

• python 3.6 (Anaconda)
• pytorch 1.0.1

Training

• 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

Testing

• 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

Fusion

in

eval.py

Results on DTU

| | 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