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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number1942
JournalRemote Sensing
Issue number8
Publication statusPublished - 17 Apr 2022


  • convolutional neural networks
  • deep learning
  • dense matching
  • end-to-end
  • pyramid architecture

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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