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 done as part of a Thesis project.

How to Use

Environment

• python >= 3.6 (Anaconda)
• pytorch >= 1.0.1

Download

The complete training and testing data can be downloaded from Gaurav-Gosain/dissertation_data (github.com).

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, I implemented a simple version of depth map fusion.

Evaluation

The matlab based evaluation code provided by the DTU Dataset is used to evaluate the results. The bare minimum code required for evaluation is present in the /Evaluation folder.

The Dissertation.ipynb is a Jupyter Notebook that contains the final code for the entire implementation.

The Dissertation_tpu.ipynb is a Jupyter Notebook that contains the code adapted to work with the pytorch lightning framework.