卫星影像重建新方法，有效减少几何失真
SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery
- 引入三种与模型无关的正则化，约束几何结构
- 在DFC2019上海拔误差降低13.9%至11.7%
- 适合需要高精度三维重建的遥感应用
我们提出SatGeo-NeRF，一种用于卫星影像的几何正则化神经辐射场。该方法通过三种模型无关的正则化，缓解当前最先进模型中存在的过拟合引发的几何伪影。重力对齐平面正则化将深度推断的近似表面法线对齐重力轴，促进局部平面性，并通过对应表面近似耦合相邻射线，实现跨射线梯度传播。粒度正则化强制粗到细的几何学习过程，深度监督正则化则稳定早期训练，提升几何精度。在DFC2019卫星重建基准测试中，SatGeo-NeRF相比EO-NeRF和EO-GS等先进基线，平均海拔误差分别降低13.9%和11.7%。
原文摘要 · Abstract (English)
We present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models using three model-agnostic regularizers. Gravity-Aligned Planarity Regularization aligns depth-inferred, approximated surface normals with the gravity axis to promote local planarity, coupling adjacent rays via a corresponding surface approximation to facilitate cross-ray gradient flow. Granularity Regularization enforces a coarse-to-fine geometry-learning scheme, and Depth-Supervised Regularization stabilizes early training for improved geometric accuracy. On the DFC2019 satellite reconstruction benchmark, SatGeo-NeRF improves the Mean Altitude Error by 13.9% and 11.7% relative to state-of-the-art baselines such as EO-NeRF and EO-GS.
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