发表机构
Wuhan University; Hubei Luojia Laboratory（武汉大学; 湖北珞珈实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3DGS重建DSM时高度层混合导致非物理高程的问题，提出风险图引导的高度层一致性高斯泼溅方法HLC-GS，通过风险图定位及可靠性校正与次要层抑制，在DFC2019和IARPA2016上超越六种SOTA方法，显著降低MAE和RMSE并提升PAG指标。
AI 中文摘要
数字表面模型（DSM）是表示地球表面高程的基础地理空间数据产品。近年来，3D高斯泼溅（3DGS）凭借其显式场景表示和高效优化，在多视角光学卫星影像的DSM重建中展现出巨大潜力。然而，在基于3DGS的DSM生成中，alpha加权的高斯高度聚合可能会在同一渲染像素或DSM采样位置混合来自不同高度层的高斯泼溅，产生非物理的中间高程和高度层混合误差。为解决此问题，我们提出了HLC-GS，一种用于光学卫星影像DSM重建的风险图引导的高度层一致性高斯泼溅方法。HLC-GS由风险图模块、主导层可靠性校正模块和次要层抑制模块组成。风险图定位具有异常高度离散性和不可靠主导层响应的高风险像素，而后两个模块则规范不可靠的主导层响应并抑制支持较弱的远次要层响应。我们在DFC2019和IARPA2016数据集上进行了大量实验。与六种最先进的DSM重建方法相比，HLC-GS实现了更好的整体精度。与最新且精度增强的EOGS相比，HLC-GS在评估场景上将平均MAE从1.46米降低到1.18米，平均RMSE从2.78米降低到2.58米，同时将PAG$_{2.5}$从86.09%提高到88.61%。总体而言，这些结果表明，显式建模逐像素高度层一致性可减轻高度层混合，并提高基于3DGS的光学卫星影像DSM重建的几何质量。
英文摘要
Robust 3D reconstruction from multi-view optical satellite imagery requires fusing complementary but sometimes conflicting geometric evidence. Digital surface models (DSMs) are the primary elevation representations for satellite-based 3D reconstruction, making reliable height estimation essential. However, in a Gaussian scene representation jointly optimized from multiple views, Gaussian responses at different elevations can support competing height hypotheses at the same rendered location, while conventional alpha-weighted elevation aggregation may produce intermediate elevations that do not correspond to physical surfaces. To address this challenge, we formulate DSM reconstruction as a reliability-aware height-hypothesis fusion problem and propose HLC-GS, a reliability-aware Height-Layer Consistency Gaussian Splatting framework for multi-view satellite 3D reconstruction. HLC-GS organizes projected Gaussian responses into candidate height hypotheses and evaluates their relative support using layer competition and Gaussian footprint support. A continuous height-layer risk map guides dominant-layer reliability correction and secondary-layer suppression during optimization. The proposed training strategy regulates conflicting Gaussian responses within the shared representation to improve the reliability of reconstructed surface elevations. Experiments on seven scenes from the DFC2019 and IARPA2016 datasets demonstrate improved DSM reconstruction accuracy. Compared with EOGS, HLC-GS reduces the average DSM MAE from 1.46~m to 1.18~m and RMSE from 2.78~m to 2.58~m, while increasing PAG$_{2.5}$ from 86.09\% to 88.61\%, with comparable computational cost.
