| Autor: |
Yuanxin Xia, Pablo d’Angelo, Friedrich Fraundorfer, Jiaojiao Tian, Mario Fuentes Reyes, Peter Reinartz |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 14, Iss 8, p 1942 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs14081942 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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