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Code for "Point-based Multi-view Stereo Network" (ICCV 2019 Oral)
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Point-based Multi-view Stereo Network


PointMVSNet is a deep point-based deep framework for multi-view stereo (MVS). PointMVSNet directly processes the target scene as point clouds and predicts the depth in a coarse-to-fine manner. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. If you find this project useful for your research, please cite:

    author = {Chen, Rui and Han, Songfang and Xu, Jing and Su, Hao},
    title = {Point-based Multi-view Stereo Network},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    year = {2019}

How to use


The environment requirements are listed as follows:

  • Pytorch 1.0.1
  • CUDA 9.0
  • CUDNN 7.4.2
  • GCC5


  • Check out the source code

    git clone && cd PointMVSNet

  • Install dependencies


  • Compile CUDA extensions



  • Download the preprocessed DTU training data from MVSNet and unzip it to data/dtu.

  • Train the network

    python pointmvsnet/ --cfg configs/dtu_wde3.yaml

    You could change the batch size in the configuration file according to your own pc.


  • Download the rectified images from DTU benchmark and unzip it to data/dtu/Eval.

  • Test with your own model

    python pointmvsnet/ --cfg configs/dtu_wde3.yaml

  • Test with the pretrained model

    python pointmvsnet/ --cfg configs/dtu_wde3.yaml TEST.WEIGHT outputs/dtu_wde3/model_pretrained.pth

Depth Fusion

PointMVSNet generates per-view depth map. We need to apply depth fusion tools/ to get the complete point cloud. Please refer to MVSNet for more details.

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