Uncertainty awareness with adaptive propagation for multi-view stereo

Training

1. Prepare DTU training set(640x512).
2. Edit config.py: set "DatasetsArgs.root_dir", "LoadDTU.train_root&train_pair".
3. Run the script for training.

python train.py  

Testing

The pre-training model in "pth".

1. Prepare DTU test set(1600x1200)(百度网盘 提取码：6au3) and Tanks and Temples dataset(百度网盘 提取码：a4oz).
2. Edit config.py: set "DatasetsArgs.root_dir", "LoadDTU.eval_root&eval_pair", and "LoadTanks.eval_root"
3. Run the script for the test.

# DTU
python eval.py -p pth/dtu_16.pth -d dtu
# Tanks and Temples
python eval.py -p pth/dtu_16.pth -d tanks

Fusion

There two methods in "tools": "filter"and "gipuma".

DTU dataset

1. Install fusibile tools: https://github.com/kysucix/fusibile
2. Edit tools/gipuma/conf.py: set "root_dir", "eval_folder" and "fusibile_exe_path".
3. Run the script.

cd tools/gipuma
python fusion.py -cfmgd

Tanks and Temples dataset

1. Run the script.

# filter
cd tools/filter
python dynamic_filter_gpu.py -e EVAL_OUTPUT_LOCATION -r DATASET_PATH -o OUTPUT_PATH 

Acknowledgements

Our work is partially baed on these opening source work: MVSNet, MVSNet-pytorch, D2HC-RMVSNet. We appreciate their contributions to the MVS community.

Citation

This work will be published in Applied Intelligence.