KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo

Code for paper KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo

Tips: If you meet any problems when reproduce our results, please contact Yikang Ding (dyk20@mails.tsinghua.edu.cn). We are happy to help you solve the problems and share our experience.

Change log

• 12.2022: Update code and README (e.g., more instruction of training and testing, update the fuse code and the pretrained model).
• 09.2022: Update code (e.g., for cross view check and prob encoding, training scripts).

Installation

Clone this repo:

git clone https://github.com/megvii-research/KD-MVS.git
cd KD-MVS

We recommend using Anaconda to manage python environment:

conda create -n kdmvs python=3.6
conda activate kdmvs
pip install -r requirements.txt

We also recommend using apex, you can install apex from the official repo.

Data preparation

Training data

Download the preprocessed DTU training data (from Original MVSNet), and unzip it to construct a dataset folder like:

dtu_training
 ├── Cameras
 └── Rectified

Testing data

Download our processed DTU testing data and unzip it as the test data folder, which should contain one cams folder, one images folder and one pair.txt file.

Training

Unsupervised training

Set the configuration in scripts/run_train_unsup.sh as:

• Set DATASET_DIR as the path of DTU training set.
• Set LOG_DIR as the path to save the checkpoints.
• Set NGPUS and BATCH_SIZE according to your machine.
• (Optional) Modify other hyper-parameters according to the argparse in train_unsup.py, such as summary_freq, save_freq, and so on.

To train your model, run:

bash scripts/run_train_unsup.sh

KD training

Note:

• We use the apex and sync_bn by default, to use these modules, make sure you have installed the apex according to the official repo.
• We use the gipuma fusion method by default, please make sure you have compiled and installed it correctly. To do so, you need clone the modified version from Yao Yao. Modify the line-10 in CMakeLists.txt to suit your GPUs. Then install it by cmake . and make, which will generate the executable at FUSIBILE_EXE_PATH.
• The number and type of GPUs (as well as batchsize) used in training phase might affect the final results. Using sync_bn could help with this problem.

To reproduce the results, please note:

• The checkpoint of the last epoch isn't always the best one. In supervised mode, we can easily pick the best model with the help of validation set. However, in self-supervised mode, we need to pick models manually based on experience.
• The performance of different epochs varies greatly, using apex and sync_bn could be helpful (but still can't handle this problem completely).
• The exprimental results reported in our paper are obtained by using different thresholds in different rounds. However, we also repoduce the results by using the latest code and the same thresholds in different rounds.

Before start training, set the configuration in scripts/run_train_kd.sh as:

• Set DATASET_DIR as the path of DTU training set.
• Set LOG_DIR as the path to save the checkpoints.
• Set CHECKED_DEPTH_DIR as the path to our pseudo label folder.
• Set NGPUS and BATCH_SIZE according to your machine.
• (Optional) Modify other hyper-parameters according to the argparse, such as summary_freq, save_freq, and so on.

run:

bash scripts/run_train_kd.sh

Testing

For easy testing, you can download our pretrained models and put them in ckpt folder, or use your own models and follow the instruction below.

Make sure:

• Download the processed test data.
• Install the fusible gipuma package.

Set the configuration in scripts/run_test_dtu.sh:

• Set TESTPATH as the path of DTU testing set.
• Set CKPT_FILE as the path of the model weights.
• Set OUTDIR as the path to save results.
• Set FUSIBILE_EXE as the path to gipuma fusible file.

Run:

bash scripts/run_test_dtu.sh

The reconstructed point cloud results would be stored in outputs/test_dtu/gipuma_pcd, you can also download our fused point cloud results of KD-trained model from here.

To get quantitative results of the fused point clouds from the official MATLAB evaluation tools, you can refer to TransMVSNet.

By using the latest code, pretrained model and default parameters, you can get the final results like:

Model	Acc.	Comp.	Overall
unsup	0.4166	0.4335	0.4251
KD	0.3674	0.2847	0.3260

Citation

@inproceedings{ding2022kdmvs,
  title={KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo},
  author={Ding, Yikang and Zhu, Qingtian and Liu, Xiangyue and Yuan, Wentao and Zhang, Haotian  and Zhang, Chi},
  booktitle={European Conference on Computer Vision},
  year={2022},
  organization={Springer}
}

Acknowledgments

We borrow some code from CasMVSNet and U-MVS. We thank the authors for releasing the source code.