3D reconstruction of spherical images: A review of techniques, applications, and prospects

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, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumergrade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-ofthe-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.

Deep Learning-Based Stereo Matching for High-Resolution Satellite Images: A Comparative Evaluation

“…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.…”