Stereo matching and depth map collection algorithm based on deep learning
Hao Xu · 2017
This paper elaborates the research on stereo matching and depth map collection algorithm based on deep learning, use the Torch deep learning framework based on Linux platform to build neural network. The neural network refers to open source algorithm to achieve the use of convolution neural network instead of the traditional algorithm to calculate the matching cost function. This paper also improves the structure of the convolution neural network by using different activation function and adding the batch normalization layer and other methods, reducing the error matching rate, and then get the disparity map and depth map by the post-processing algorithm which includes the matching cost aggregation, disparity computation, disparity refinement. Then the paper verified the effect of the algorithm by experiment and analyzed the experiment results, and used the Middlebury stereo algorithm evaluation platform to evaluate the algorithm. And finally the porposed algorithm gets better stereo matching effect than before.