README.md

AdaptMVSNet

Paper

NanHuLab Affiliation

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installion

• These project ned to be installed: torch 1.12.1, python 3.8, cuda >= 10.1
• Please install Tensorrt, if you want to test in fast inference.

pip install -r requirements.txt

Download

• Preprocessed training/validation data: DTU's evaluation set ,BlendedMVS ,Tanks&Temples. Each dataset is already organized as follows:

root_directory
├──scan1 (scene_name1)
├──scan2 (scene_name2) 
      ├── images                 
      │   ├── 00000000.jpg       
      │   ├── 00000001.jpg       
      │   └── ...                
      ├── cams                   
      │   ├── 00000000_cam.txt   
      │   ├── 00000001_cam.txt   
      │   └── ...                
      └── pair.txt  

Testing

• running eval.sh, using dtu dataset
• In eval.sh, set DTU_TESTING, ETH3D_TESTING or TANK_TESTING as the root directory of corresponding dataset and uncomment the evaluation command for corresponding dataset (default is to evaluate on DTU's evaluation set). If you want to change the output location (default is same as input one), modify the --output_folder parameter.
• CKPT_FILE is the checkpoint file (our pretrained model is ./checkpoints/params_000007.ckpt), change it if you want to use your own model.
• Test on GPU by running sh eval.sh. The code includes depth map estimation and depth fusion. The outputs are the point clouds in ply format.
• In evaluations/dtu/BaseEvalMain_web.m, set dataPath as path to SampleSet/MVS Data/, plyPath as directory that stores the reconstructed point clouds and resultsPath as directory to store the evaluation results. Then run evaluations/dtu/BaseEvalMain_web.m in matlab.
• The results look like:

  Acc.(mm)	Comp.(mm)	Overall.(mm)
  0.445	0.257	0.351

Training

• Download pro-processed DTU's training set.
• In train.sh, set MVS_TRAINING as the root directory of the converted dataset; set --output_path as the directory to store the checkpoints.
• Train the model by running sh train.sh.