ModuleNet: A Convolutional Neural Network for Stereo Vision

Convolutional Neural Networks (CNN) has gained much attention for the solution of numerous vision problems including disparities calculation in stereo vision systems. In this paper, we present a CNN based solution for disparities estimation that builds upon a basic module (BM) with limited range of disparities that can be extended using various BM in parallel. Our BM can be understood as a segmentation by disparity and produces an output channel with the memberships for each disparity candidate, additionally the BM computes a channel with the out–of–range disparity regions. This extra channel allows us to parallelize several BM and dealing with their respective responsibilities. We train our model with the MPI Sintel dataset. The results show that ModuleNet, our modular CNN model, outperforms the baseline algorithm Efficient Large-scale Stereo Matching (ELAS) and FlowNetC achieving about a 80% of improvement.