Pytorch 0.4.0 (The codes for (0.3.0 or 1.0) are in the other brach)
CUDA 9.0
python 3.6.4
scipy
argparse
tensorboardX
progressbar2
blessings
path.py

opencv
imageio
joblib
h5py
lz4

bash download_traindata.sh
python ./dataset/preparation/preparedata_train.py

bash download_testdata.sh
python ./dataset/preparation/preparedata_test.py

python train.py ./dataset/train/ --mindepth 0.5 --nlabel 64 --log-output

python test.py ./dataset/test/ --sequence-length 2 --output-print --pretrained-dps ./pretrained/dpsnet.pth.tar

python test_ETH3D.py ./dataset/ETH3D_results/ --sequence-length 3 --output-print --pretrained-dps ./pretrained/dpsnet.pth.tar

Paper (epoch 4) -> Update (epoch 10)
MVS    A.Rel  A.diff Sq.Rel  RMSE  R. log   a=1    a=2    a=3
Paper  0.0722 0.2095 0.0798 0.4928 0.1527 0.8930 0.9502 0.9760
Update 0.0813 0.2006 0.0971 0.4419 0.1595 0.8853 0.9454 0.9735
SUN3D  A.Rel  A.diff Sq.Rel  RMSE  R. log   a=1    a=2    a=3
Paper  0.1470 0.3234 0.1071 0.4269 0.1906 0.7892 0.9317 0.9672
Update 0.1469 0.3355 0.1165 0.4489 0.1956 0.7812 0.9260 0.9728
RGBD   A.Rel  A.diff Sq.Rel  RMSE  R. log   a=1    a=2    a=3
Paper  0.1538 0.5235 0.2149 0.7226 0.2263 0.7842 0.8959 0.9402
Update 0.1508 0.5312 0.2514 0.6952 0.2421 0.8041 0.8948 0.9268
Scenes A.Rel  A.diff Sq.Rel  RMSE  R. log   a=1    a=2    a=3
Paper  0.0558 0.2430 0.1435 0.7136 0.1396 0.9502 0.9726 0.9804
Update 0.0500 0.1515 0.1108 0.4661 0.1164 0.9614 0.9824 0.9880