用元学习实现卫星影像快速3D重建，单次前向传播完成表面恢复。
SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery
- 通过元学习捕获可迁移先验，单次前向传播完成3D重建。
- 在多个数据集上实现高精度数字表面模型重建，计算成本降低显著。
- 适合需要快速响应的灾后监测与环境变化追踪场景。
从多时相卫星影像中快速、大规模地进行3D重建对环境监测、城市规划和灾害响应至关重要，但受限于光照变化、传感器异质性以及逐场景优化的成本。我们提出SwiftGS，一种元学习系统，通过预测解耦几何-辐射的高斯原型与轻量级SDF，仅需一次前向传播即可完成3D表面重建，取代昂贵的逐场景拟合。模型结合可微分物理图（投影、光照、传感器响应）与空间门控机制，融合稀疏高斯细节与全局SDF结构，引入语义-几何融合、条件轻量任务头及来自冻结几何教师的多视角监督，并在不确定性感知的多任务损失下训练。推理时，SwiftGS支持零样本推断，可选紧凑标定，实现高精度数字表面模型重建与视图一致渲染，计算成本显著降低；消融实验验证了混合表示、物理感知渲染与周期性元训练的有效性。
原文摘要 · Abstract (English)
Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster response, yet remains difficult due to illumination changes, sensor heterogeneity, and the cost of per-scene optimization. We introduce SwiftGS, a meta-learned system that reconstructs 3D surfaces in a single forward pass by predicting geometry-radiation-decoupled Gaussian primitives together with a lightweight SDF, replacing expensive per-scene fitting with episodic training that captures transferable priors. The model couples a differentiable physics graph for projection, illumination, and sensor response with spatial gating that blends sparse Gaussian detail and global SDF structure, and incorporates semantic-geometric fusion, conditional lightweight task heads, and multi-view supervision from a frozen geometric teacher under an uncertainty-aware multi-task loss. At inference, SwiftGS operates zero-shot with optional compact calibration and achieves accurate DSM reconstruction and view-consistent rendering at significantly reduced computational cost, with ablations highlighting the benefits of the hybrid representation, physics-aware rendering, and episodic meta-training.
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