GA-Net-Pyramid: An Efficient End-to-End Network for Dense Matching

Yuanxin Xia*, Pablo D’angelo, Friedrich Fraundorfer, Jiaojiao Tian, Mario Fuentes Reyes, Peter Reinartz

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung


Dense matching plays a crucial role in computer vision and remote sensing, to rapidly provide stereo products using inexpensive hardware. Along with the development of deep learning, the Guided Aggregation Network (GA-Net) achieves state-of-the-art performance via the proposed Semi-Global Guided Aggregation layers and reduces the use of costly 3D convolutional layers. To solve the problem of GA-Net requiring large GPU memory consumption, we design a pyramid architecture to modify the model. Starting from a downsampled stereo input, the disparity is estimated and continuously refined through the pyramid levels. Thus, the disparity search is only applied for a small size of stereo pair and then confined within a short residual range for minor correction, leading to highly reduced memory usage and runtime. Tests on close-range, aerial, and satellite data demonstrate that the proposed algorithm achieves significantly higher efficiency (around eight times faster consuming only 20–40% GPU memory) and comparable results with GA-Net on remote sensing data. Thanks to this coarse-to-fine estimation, we successfully process remote sensing datasets with very large disparity ranges, which could not be processed with GA-Net due to GPU memory limitations.

FachzeitschriftRemote Sensing
PublikationsstatusVeröffentlicht - 17 Apr. 2022

ASJC Scopus subject areas

  • Erdkunde und Planetologie (insg.)


Untersuchen Sie die Forschungsthemen von „GA-Net-Pyramid: An Efficient End-to-End Network for Dense Matching“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren