An Overview of GANet - Guided Aggregation Net for End-to-end Stereo Matching
Alvaro Gómez
⚠ This is a preprint. It may change before it is accepted for publication.


Guided Aggregation Net for End-to-end Stereo Matching (GANet) is a stereo matching method that uses Deep Neural Networks (DNN) to compute a disparity map from a pair of images of a scene. As other classic and DNN stereo methods, it follows the traditional stereo steps: dense features are extracted from both images, the cost of matching the features at different disparities is organized in a Cost Volume (CV) which is regularized by aggregation and local filtering and finally a map with minimal cost is derived from the CV. In GANet, the aggregation of the CV is done by a Semi-Global Guided Aggregation layer (SGA) which implements a differentiable approximation of the well known Semi-Global Matching (SGM) algorithm. SGA is followed by a Local Guided Aggregation layer (LGA) that performs a local filtering. SGA and LGA weights are generated by an auxiliary guidance subnet fed with the original reference image and its extracted features. This article presents an overview of GANet. An online demo, running on CPU, is made available.