This official repository is established for Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation (ECCV2020) paper

• python 3.6
• Pytorch >= 1.0.0
• CUDA >= 9.0

./conda_install.sh

• Download the preprocessed DTU training data (also available at Baiduyun, code: s2v2), and upzip it as the MVS_TRANING folder (orrowed from MVSNet(https://raw.githubusercontent.com/YoYo000/MVSNet)).
• Set dtu_data_root to your MVS_TRAINING path in env.sh Create a log folder and a model folder in wherever you like to save the training outputs. Set the log_dir and save_dir in train.sh correspondingly.
• Train VA-MVSNet (GTX1080Ti): ./train.sh

• Download the test data for scan9 and unzip it as the TEST_DATA_FOLDER folder, which should contain one cams folder, one images folder and one pair.txt file.
• Download the pre-trained VA-MVSNet models and upzip the file as MODEL_FOLDER
• In eval_pyramid.sh, set MODEL_FOLDER to ckpt and model_ckpt_index to checkpoint_list.
• Run ./eval_pyramid.sh.

• We utilize depthfusion_pytorch.py script for Fusion (from MVSNet-pytorch).
• Set use_mmp as True to use Multi-metric Pyramid Depth Aggregation in tools/postprocess.sh.
• Enter to ./tools directory, then run ./postprocess.sh to generate final point cloud.

PVA-MVSNet point cloud results with full post-processing are also provided: DTU evaluation point clouds with extracting code zau7.

Please ref to leaderboard.

If you find this project useful for your research, please cite:

@inproceedings{yi2020PVAMVSNET,
  title={Pyramid multi-view stereo net with self-adaptive view aggregation},
  author={Yi, Hongwei and Wei, Zizhuang and Ding, Mingyu and Zhang, Runze and Chen, Yisong and Wang, Guoping and Tai, Yu-Wing},
  booktitle={ECCV},
  year={2020}
}

Thanks Xiaoyang Guo for his contribution to re-implementation of MVSNet-pytorch. Thanks Yao Yao for his previous works MVSNet/R-MVSNet.