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- Title
Constraining the Geometry of NeRFs for Accurate DSM Generation from Multi-View Satellite Images.
- Authors
Wan, Qifeng; Guan, Yuzheng; Zhao, Qiang; Wen, Xiang; She, Jiangfeng
- Abstract
Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth's surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF's superiority in accurate DSM generation from multi-view satellite images.
- Subjects
DIGITAL elevation models; REMOTE-sensing images; SURFACE of the earth; GEOMETRIC approach; POINT cloud
- Publication
ISPRS International Journal of Geo-Information, 2024, Vol 13, Issue 7, p243
- ISSN
2220-9964
- Publication type
Article
- DOI
10.3390/ijgi13070243