SatSurfGS: 基于2D高斯散射的通用稀疏视图卫星表面重建
SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction
AI总结：
本文提出SatSurfGS方法，通过构建粗到细的高斯属性预测框架，解决稀疏视图卫星表面重建中的多视图匹配可靠性问题，提升重建精度和泛化能力。
摘要：
稀疏视图卫星图像表面重建仍极具挑战性，根本原因在于多视图匹配在卫星成像条件下的可靠性在空间上高度异质。受大光度差异、弱纹理和重复纹理的影响，多视图几何约束通常稀疏、分布不均且局部不可靠。尽管2D高斯散射（2DGS）比3D高斯散射（3DGS）更适合表示连续表面，但针对稀疏视图卫星表面重建的通用前馈2DGS框架研究仍不足。为解决此问题，我们提出SatSurfGS，一种基于2DGS的通用稀疏视图卫星图像表面重建方法。所提方法构建了粗到细的高斯属性预测框架，并在三个层面显式建模局部几何可靠性：特征学习、高斯参数估计和训练优化。具体而言，我们提出了一种自信度感知的单目多视图特征融合模块，根据局部自信度自适应整合单目先验和多视图匹配特征；一个跨阶段自一致性残差引导模块，利用前一阶段渲染高度图与当前阶段MVS高度图之间的残差以及自信度信息，稳定阶段间的高斯参数细化；以及一个自信度双向路由损失，实现几何和外观监督的差异化分配。在卫星数据集上的实验表明，所提方法在渲染质量、表面重建精度、跨数据集泛化能力和推理效率方面，相比代表性通用基线和竞争性单场景优化方法均有提升。
英文摘要：
Sparse-view satellite image surface reconstruction remains highly challenging, fundamentally because the reliability of multi-view matching under satellite imaging conditions is strongly spatially heterogeneous. Affected by large photometric differences, weak textures, and repetitive textures, multi-view geometric constraints are often sparse, unevenly distributed, and locally unreliable. Although 2D Gaussian Splatting (2DGS) is more suitable than 3D Gaussian Splatting (3DGS) for the explicit representation of continuous surfaces, research on generalizable feed-forward 2DGS frameworks for sparse-view satellite surface reconstruction is still lacking. To address this issue, we propose SatSurfGS, a generalizable sparse-view surface reconstruction method for satellite imagery based on 2DGS. The proposed method builds a coarse-to-fine Gaussian attribute prediction framework and explicitly models local geometric reliability at three levels: feature learning, Gaussian parameter estimation, and training optimization. Specifically, we propose a confidence-aware monocular multi-view feature fusion module to adaptively integrate monocular priors and multi-view matching features according to local confidence; a cross-stage self-consistency residual guidance module to stabilize stage-wise Gaussian parameter refinement using the residual between the rendered height map from the previous stage and the current-stage MVS height map, together with confidence information; and a confidence bidirectional routing loss to achieve differentiated allocation of geometric and appearance supervision. Experiments on satellite datasets show that the proposed method achieves improved rendering quality, surface reconstruction accuracy, cross-dataset generalization, and inference efficiency compared with representative generalizable baselines and competitive per-scene optimization methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
