[IPOL discuss] [IPOL announce] new article: An Overview of GANet - Guided Aggregation Net for End-to-end Stereo Matching

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2023/441/

Alvaro Gómez,
An Overview of GANet - Guided Aggregation Net for End-to-end Stereo 
Matching,
Image Processing On Line, 13 (2023), pp. 215–226.
https://doi.org/10.5201/ipol.2023.441

Abstract
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.




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