Abstract:3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently,… Show more
“…Its core task is to establish the pixel-by-pixel correspondences between two images to recover the 3D information of the target (Geiger et al, 2010). Stereo dense matching has become the most crucial component in many tasks that range from localization tracking to 3D reconstruction (Li et al, 2023b, Jiang et al, 2023, Geiger et al, 2011, He et al, 2021. As the popularity and quality of satellite images continue to improve, stereo matching based on high-resolution satellite images has been widely used in various applications, such as 3D modeling of large-scale cities (Zhang et al, 2022, Facciolo et al, 2017, Huang et al, 2017.…”
Abstract. Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.
“…Its core task is to establish the pixel-by-pixel correspondences between two images to recover the 3D information of the target (Geiger et al, 2010). Stereo dense matching has become the most crucial component in many tasks that range from localization tracking to 3D reconstruction (Li et al, 2023b, Jiang et al, 2023, Geiger et al, 2011, He et al, 2021. As the popularity and quality of satellite images continue to improve, stereo matching based on high-resolution satellite images has been widely used in various applications, such as 3D modeling of large-scale cities (Zhang et al, 2022, Facciolo et al, 2017, Huang et al, 2017.…”
Abstract. Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.
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