深度估计-使用Pytorch实现的实时多视图深度估计算法-优质项目实战.zip

# MVDepthNet ## A Real-time Multiview Depth Estimation Network 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 <img src="fig/example.png" alt="MVDepthNet example" width = "320*2" height = "100"> ## 1.0 Prerequisites + **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** ## 2.0 Download the model parameters and the samples UPDATE: the dropbox link has failed because of the large traffic. This is the BaiduPan link: model weight: ```链接: https://pan.baidu.com/s/1CjV6iWBbjWOxGetf2ZXStQ 提取码: gbfg``` and sample data: ```链接: https://pan.baidu.com/s/1feYfF6qSd7z7_anmR_rgnQ 提取码: g1fo```. 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](https://www.dropbox.com/s/o1n1w0chlrw4lqt/opensource_model.pth.tar?dl=0) and the sample images via [the link](https://www.dropbox.com/s/hr59f24byc3x8z3/sample_data.pkl.tar.gz?dl=0). Put the model ```opensource_model.pth.tar``` and extract the ```sample_data.pkl.tar.gz``` under the project folder. ## 3.0 Run the example Just ```python example.py``` ## 4.0 Use your own data 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. ### 4.1 Use multiple images 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.