Christy61 / OpenMaterial

An example use of the openmaterial dataset

Benchmarking_Everything

1. download dataset

Get your own token

1. Click on your avatar in the upper right corner and select "Settings".
2. On the "Settings" page, click "Access Tokens" on the left side.
3. Generate a new Token and copy it.

python download.py --token <your-token> --type all

after running this, The following file structure is obtained

datasets
├── groundtruth
│   ├── 5c4ae9c4a3cb47a4b6273eb2839a7b8c
│       └── clean_5c4ae9c4a3cb47a4b6273eb2839a7b8c.ply
│   ├── 5c0514eae1f94f22bc5475fe0970cd28
│       └── clean_5c0514eae1f94f22bc5475fe0970cd28.ply
│   └── ... 
├── openmaterial
│   ├── 5c4ae9c4a3cb47a4b6273eb2839a7b8c
│       ├── train
│           ├── images
│           ├── mask
│       ├── test
│       ├── transforms_train.json
│       └── transforms_test.json
│   ├── 5c0514eae1f94f22bc5475fe0970cd28
│   └── ... 

If you need to use depth, please use the following command:

python download.py --token <your-token> --type all --depth

an example for using depth data (Here are the real depth values, not normalised):

with h5py.File(filename, 'r') as hdf:
    dataset = hdf['depth']
    depth = dataset[:]  # size: (1200, 1600) 

2. start training

for ${method}: method can be instant-nsr-pl and NeuS2

cd ${method}
chmod +x run_openmaterial.sh
bash run_openmaterial.sh $gpu 
# for example: bash run_openmaterial.sh 0
cd ../

the result of nerf are stored in the "instant-nsr-pl-output-womask/output.txt" in the following format:

${object}:${method}:${material}:${PSNR}-${SSIM}

3. Eval

If you want to use scripts, make sure you store the mesh in the following file format:

Mesh
├── instant-nsr-pl-wmask
│   ├── meshes
│       ├── 5c4ae9c4a3cb47a4b6273eb2839a7b8c
│           └── diffuse.ply
│       ├── 5c0514eae1f94f22bc5475fe0970cd28
│           └── diffuse.ply
│       └── ... 

eval after training with all methods

cd Openmaterial-main
chmod +x eval/eval.sh
bash eval/eval.sh ../Mesh ../output
cd ../

the result are stored in the "mesh_evaluation/output.txt" in the following format:

${object}:${method}:${material}:${cds}

Run the following command to integrate the results:

python sum_metrics.py