Georecon: a coarse-to-fine visual 3D reconstruction approach for high-resolution images with neural matching priors
| Autor: | Weijia Bei, Xiangtao Fan, Hongdeng Jian, Xiaoping Du, Dongmei Yan, Jianhao Xu, Qifeng Ge |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | International Journal of Digital Earth, Vol 17, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 17538947 1753-8955 1753-8947 |
| DOI: | 10.1080/17538947.2024.2421956 |
| Popis: | Visual 3D reconstruction enables rebuilding 3D scenes from captured images, serving as a fundamental data source for digital earth modeling and intelligent cities. In the foundational step, recent methods leverage learning-based descriptors for image registration and achieve tremendous advances in precision and robustness. However, these methods inevitably execute down sampling towards high-resolution images to fit the needs of neural networks, which leads to precision degradation of feature localization and matching. Thus, we propose GeoRecon: a novel coarse-to-fine visual 3D reconstruction method that optimally utilizes high-resolution images for high-quality visual 3D reconstruction. Firstly, the coarse stage conducts coarse reconstruction from downsampled images by performing neural matching with geometric priors. Secondly, we define the fine-grained stage, proposing a GPU-based algorithm for generating image-patch correspondences based on the neural matching priors to perform fine-grained image registration. Finally, based on the optimized camera poses under this coarse-to-fine paradigm, progressive dense reconstruction leveraging efficient neural radiance fields is proposed to accomplish the high-quality MVS reconstruction. Comparative experiments across various scenarios demonstrate the proposed method’s superior precision, robustness, and reconstruction quality. |
| Databáze: | Directory of Open Access Journals |
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