# MVSNet_pytorch

**Repository Path**: dufolk/MVSNet_pytorch

## Basic Information

- **Project Name**: MVSNet_pytorch
- **Description**: PyTorch Implementation of MVSNet
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 3
- **Created**: 2021-07-30
- **Last Updated**: 2023-11-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# An Unofficial Pytorch Implementation of MVSNet

[MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://arxiv.org/abs/1804.02505). 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](https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view) (Fixed training cameras, from [Original MVSNet](https://github.com/YoYo000/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](https://drive.google.com/open?id=135oKPefcPTsdtLRzoDAQtPpHuoIrpRI_) (from [Original MVSNet](https://github.com/YoYo000/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](https://drive.google.com/file/d/1j2I_LNKb9JeCl6wdA7hh8z1WgVQZfLU9/view?usp=sharing).
* Test MVSNet: ``./test.sh``

### Fusion

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


## 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``, the fusion code is also different from the original repo.