这篇论文用旧DEM数据低成本做高精度3D地形重建，比LiDAR省钱，比纯图像方法更准，还实时。搞野火应急或地理信息的人可以看看。
该论文提出LTM，一种多模态重建框架，利用过时的数字高程模型（DEM）作为几何先验，通过物理像素对齐替代传统特征匹配，计算复杂度大幅降低。在基于真实野火区域的模拟器上，方法生成高保真深度图并保持实时性能。相比现有技术，重建精度和计算效率均有显著提升。
LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfire-prone regions often span vast areas where conventional reconstruction methods underperform. Airborne LiDAR systems provide high-resolution terrain data, but they are expensive and infrequently updated. Image-based methods offer a lower-cost alternative, but struggle due to sparse visual features and limited image overlap. We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction. Our key innovation is physics-based pixel-pixel alignment between images and DEM data, dramatically reducing computational complexity by eliminating expensive feature matching procedures. To validate our approach, we developed a large-terrain simulator based on a real wildfire-prone area, generating realistic images enabling a comprehensive evaluation. Given posed images and legacy DEMs, our method produces high-fidelity depth maps while maintaining real-time performance. We find significant improvements in reconstruction accuracy and computational efficiency over existing techniques, offering a scalable solution for wildfire response.