Pyramid Stereo Matching Network

Understand

Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs).

• However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions.
• To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN.
• The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume.