Multi-distribution fitting for multi-view stereo

Training

1. Prepare DTU training set(640x512) and BlendedMVS dataset(768x576).
2. Edit config.py and set "DatasetsArgs.root_dir", "LoadDTU.train_root&train_pair", and "LoadBlendedMVS.train_root".
3. Run the script for training.

# DTU
python train.py -d dtu 
# BlendedMVS
python train.py -d blendedmvs

Testing

The Pre-training model in "pth".

1. Prepare DTU test set(1600x1200)(Baidu Netdisk code：6au3) and Tanks and Temples dataset(Baidu Netdisk code：a4oz).
2. Edit config.py and 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_29.pth.pth -d dtu
# Tanks and Temples
python eval.py -p pth/blendedmvs_29.pth -d tanks

Fusion

There three methods in "tools": "filter", "gipuma", and "pcd".

DTU dataset

1. Install fusibile tools: tools/fusibile or https://github.com/kysucix/fusibile
2. Edit tools/gipuma/conf.py and 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(main method)
cd tools/filter
python dynamic_filter_gpu.py -e EVAL_OUTPUT_LOCATION -r DATASET_PATH -o OUTPUT_PATH 
# pcd
cd tools/pcd
chmod +x ninja_init.sh
source ninja_init.sh
python fusion.py -e EVAL_OUTPUT_LOCATION -r DATASET_PATH -o OUTPUT_PATH 

Results (single Quadro RTX 5000)

DTU dataset

	Acc(mm)	Comp(mm)	Overall(mm)	Time(s/view)	Memory(M)
MDFNet(4scale)	0.349	0.303	0.326	0.376	4396

The mean F-score on Tanks and Temples dataset

	intermediate	advanced
MDFNet(4scale)	56.18	34.70
MDFNet(3scale)	60.24	37.31

example

Acknowledgements

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

Citation

This work will be published in Machine Vision and Applications.