SatSplatDiff combines depth supervision and shadow-guided generative refinement with 2DGS to reduce geometric MAE by up to 18% and improve visual fidelity by 28-45% on satellite datasets while enabling 5x resolution enhancement.
Sparsesat-nerf: Dense depth supervised neural radiance fields for sparse satellite images
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SatSurfGS improves sparse-view satellite surface reconstruction accuracy and generalization by adding confidence-aware monocular-multi-view fusion, cross-stage residual guidance, and bidirectional routing loss to 2D Gaussian Splatting.
A technique reconstructs large urban areas from sparse extreme off-nadir satellite images by modeling geometry as a Z-monotonic 2.5D height map SDF and applying a generative network to restore plausible textures on the resulting mesh.
citing papers explorer
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SatSplatDiff: Geometry-preserving generative refinement for high-fidelity satellite Gaussian Splatting
SatSplatDiff combines depth supervision and shadow-guided generative refinement with 2DGS to reduce geometric MAE by up to 18% and improve visual fidelity by 28-45% on satellite datasets while enabling 5x resolution enhancement.
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SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction
SatSurfGS improves sparse-view satellite surface reconstruction accuracy and generalization by adding confidence-aware monocular-multi-view fusion, cross-stage residual guidance, and bidirectional routing loss to 2D Gaussian Splatting.
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From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images
A technique reconstructs large urban areas from sparse extreme off-nadir satellite images by modeling geometry as a Z-monotonic 2.5D height map SDF and applying a generative network to restore plausible textures on the resulting mesh.