用阴影引导生成修复，让卫星3D重建更真实且不歪形。
SatSplatDiff: Geometry-preserving generative refinement for high-fidelity satellite Gaussian Splatting
- 引入单目深度监督与多尺度几何优化，确保表面准确。
- 用计算的阴影图指导生成修复，减少视觉幻觉和几何失真。
- 适合大范围卫星3D重建，支持高分辨率、跨区块无缝拼接。
高斯点阵最近被用于卫星三维重建，能高效表示辐射多样场景。但因卫星视角有限，建筑立面监督不足，导致表面缺损、视觉质量下降。生成式修复通过预训练生成先验迭代优化渲染图像作为监督信号，提升视觉质量。然而现有方法独立优化每张视图，易产生幻觉并破坏照片一致性，引发几何退化。为此，我们提出SatSplatDiff，旨在最小化生成修复中的几何退化。基于前期工作SatSplat的摄影测量DSM初始化与2DGS阴影投射，我们引入单目深度监督和多尺度几何精修，建立几何精确且正则化的表面表示。随后采用阴影引导的生成修复：由几何计算的阴影图指导高斯点保持与底层结构一致，从而在提升视觉质量的同时降低几何失真。在IARPA2016和DFC2019数据集上的实验表明，本方法达到最先进性能，几何平均绝对误差（MAE）降低最多18%，视觉质量（FID-CLIP）提升28–45%。方法可实现最高5倍分辨率增强，幻觉极少，外观与传感器一致，具备跨瓦片无缝衔接能力与强可扩展性。源码见https://github.com/GDAOSU/SatSplatDiff。
原文摘要 · Abstract (English)
Gaussian Splatting has been recently explored for satellite 3D reconstruction, demonstrating flexibility and efficiency in representing radiometrically diverse satellite scenes. However, the limited top viewpoint of satellite imagery results in insufficient supervision on building facades, leaving surface holes and degraded visual fidelity. Generative refinement, which leverages pretrained generative priors to iteratively refine and update the rendered images used as supervision targets, has recently been investigated to improve the visual fidelity of Gaussian-rendered images. However, since these models refine each view independently, the resulting images can generate hallucinations and break photo-consistency, leading to geometric degradation. To address these limitations, we propose SatSplatDiff, which aims to minimize geometric degradation prevalent in generative refinement. Building on photogrammetric DSM initialization and 2DGS-based shadow casting established in our prior work SatSplat, we first introduce monocular depth supervision and multi-scale geometric refinement to establish a geometrically accurate and well-regularized surface representation. We then apply shadow-guided generative refinement, where geometrically calculated shadow maps guide the Gaussians to maintain consistency with the underlying geometry, improving visual fidelity while reducing geometric degradation. Extensive evaluations on the IARPA2016 and DFC2019 datasets demonstrate state-of-the-art performance, reducing geometric MAE by up to 18% and improving visual fidelity (FID-CLIP) by 28-45% over existing baselines. Our method delivers up to 5x resolution enhancement with minimal hallucination and sensor-consistent appearance, demonstrating seamless cross-tile consistency and strong scalability for large-scale reconstruction. Source code is available at https://github.com/GDAOSU/SatSplatDiff
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