MVSNet_pytorch:MVSNet的PyTorch实现

# 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.