用旧数字高程图辅助图像重建地形，提升火灾区三维地图精度与速度。
LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
- 用旧的数字高程图作几何先验，避免复杂特征匹配。
- 在真实火灾区数据上实现高保真深度图与实时性能。
- 适合应急响应、遥感测绘等需要快速生成地形的场景。
准确的三维地形图对评估火灾风险至关重要。然而，火灾多发区域范围广，传统重建方法效果不佳。机载激光雷达虽可提供高分辨率数据，但成本高且更新频率低。基于图像的方法成本较低，但受限于视觉特征稀疏和图像重叠不足。本文提出一种多模态重建框架，利用过时的数字高程模型（DEMs）作为几何先验，实现图像与DEM间的物理驱动像素级对齐，大幅降低计算复杂度，无需昂贵的特征匹配。为验证方法，我们基于真实火灾易发区构建了大规模地形模拟器，生成逼真的图像以进行综合评估。给定带有姿态信息的图像和历史DEM，本方法能生成高保真深度图，并保持实时性能。实验表明，该方法在重建精度和计算效率上均显著优于现有技术，为火灾应急响应提供了可扩展的解决方案。
原文摘要 · Abstract (English)
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 We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction.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.
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