Hi,
I have been working on a project recently that estimates depth map from a pair of stereo images using a fully convolutional network (https://github.com/LouisFoucard/StereoConvNet). Would you be interested in adding that as an example? It is fairly short, and it uses batch normalization so that additionally could be an example that shows implementation of batch normalization.
The data (stereo images and depth maps) used to trained the network is obtained by generating random 3d scenes with Blender, and is also available on github (https://github.com/LouisFoucard/DepthMap_dataset).
Please let me know if that is of interest to you, and I can clean it up a bit, add some more comments.
Hi, I have been working on a project recently that estimates depth map from a pair of stereo images using a fully convolutional network (https://github.com/LouisFoucard/StereoConvNet). Would you be interested in adding that as an example? It is fairly short, and it uses batch normalization so that additionally could be an example that shows implementation of batch normalization. The data (stereo images and depth maps) used to trained the network is obtained by generating random 3d scenes with Blender, and is also available on github (https://github.com/LouisFoucard/DepthMap_dataset). Please let me know if that is of interest to you, and I can clean it up a bit, add some more comments.