This is an open source implementation for 3DV 2018 submission "MVDepthNet: real-time multiview depth estimation neural network" by Kaixuan Wang and Shaojie Shen. arXiv link. If you find the project useful for your research, please cite:

@InProceedings{mvdepthnet,
    author       = "K. Wang and S. Shen",
    title        = "MVDepthNet: real-time multiview depth estimation neural network",
    booktitle    = "International Conference on 3D Vision (3DV)",
    month        = "Sep.",
    year         = "2018",
  }

Given multiple images and the corresponding camera poses, a cost volume is firstly calculated and then combined with the reference image to generate the depth map. An example is

From left to right is: the left image, the right image, the "ground truth" depth from RGB-D cameras and the estimated depth map.

A video can be used to illustrate the performance of our system:

• PyTorch

The PyTorch version used in the implementation is 0.3. To use the network in higher versions, only small changes are needed.

• OpenCV

• NumPy

We provide a trained model used in our paper evaluation and some images to run the example code.

Please download the model via the link and the sample images via the link. Put the model opensource_model.pth.tar and extract the sample_data.pkl.tar.gz under the project folder.

Just

python example.py

To use the network, you need to provide a left image, a right image, camera intrinsic parameters and the relative camera pose. Images are normalized using the mean 81.0 and the std 35.0, for example

normalized_image = (image - 81.0)/35.0.

We here provide the file example2.py to shown how to run the network using your own data. the left_pose and right_pose is the camera pose in the world frame. we show left_image, right_image, and the predicted depth in the final visualization window. A red dot in the left_image is used to test the relative pose accuracy. The red line in the right_image is the epiploar line that it much contains the red dot in the left_image. Otherwise, the pose is not accurate. You can change the position of the tested point in line 56.

To get good results, images should have enough translation and overlap between each other. Rotation dose not help in the depth estimation.

Please refer to depthNet_model.py, use the function getVolume to construct multiple volumes and average them. Input the model with the reference image and the averaged cost volume to get the estimated depth maps.

Most of the training data and test data are collected by DeMoN and we thank their work.