PyTorch implement of “DSC-MVSNet: Attention Aware Cost Volume Regularization Based On Depthwise Separable Convolution for Multi-View Stereo”.
pip install -r requirments.txt
Download the preprocessed test data DTU testing data (from Original MVSNet) and unzip it as the DTU_TESTING
folder, which should contain one cams
folder, one images
folder and one pair.txt
file.
Test with the pretrained model
python dscmvsnet/test.py --cfg configs/dtu.yaml TEST.WEIGHT outputs/pretrained.pth
Please set the following configuration
OUTPUT_DIR: "" # logfile and .pth save path
DATA:
TRAIN:
ROOT_DIR: "" # training set path
NUM_VIRTUAL_PLANE:
VAL:
ROOT_DIR: "" # validation set path
TEST:
ROOT_DIR: "" # testing set path
NUM_VIEW:
IMG_HEIGHT:
IMG_WIDTH:
NUM_VIRTUAL_PLANE:
INTER_SCALE: 2.13
MODE: "dtu" # dtu or tanks
TEST:
WEIGHT: "" # .pth path
BATCH_SIZE: 1
We need to apply depth fusion tools/depthfusion.py
to get the complete point cloud. Please refer to MVSNet for more details. And use tools/rename_ply.py
to get the rename results.
python tools/depthfusion.py -f dtu -n flow2
python tools/rename_ply.py
To obtain the fusibile:
- Check out the modified version fusibile
git clone https://github.com/YoYo000/fusibile
- Install fusibile by
cmake .
andmake
, which will generate the executable atFUSIBILE_EXE_PATH
We need to download the official STL point clouds for our evaluation. Please download the STL Point Clouds, which is the STL reference point clouds for all the scenes. And please download the observability masks and evaluation codes from the SampleSet for evaluation.