---
title: "LTM: 面向野火易发景观的大规模地形模型"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.08711v1"
permalink: "https://feed.astralor.com/items/itm_6fb27610474373f7cde17637"
published: "2026-07-09T17:22:35.000Z"
score: 77
channel: "ai"
tags: ["LTM","多模态重建","数字高程模型","计算机视觉","野火灾难"]
---

# LTM: 面向野火易发景观的大规模地形模型

## AI 点评

> 这篇论文巧妙地利用过时DEM作为先验，解决了图像重建中的稀疏特征问题，且无需昂贵LiDAR。对于地理空间AI和应急响应领域的研究者与从业者具有直接参考价值，特别适合希望在低成本下获取实时地形数据的消防部门。

## 速览

野火应急响应需要高精度3D地形图，但传统方法成本高或精度低。本文提出LTM框架，利用过时数字高程模型(DEM)作为几何先验，通过物理基础的像素-像素对齐实现图像与DEM融合，无需昂贵特征匹配，在真实野火区域模拟器上实时生成高保真深度图，显著提升重建精度和计算效率。

## 关键点

- 提出LTM框架，将过时DEM作为几何先验与图像融合进行三维重建。
- 创新点：物理基础的像素级对齐，避免特征匹配，降低计算复杂度。
- 在基于真实野火区域的大地形模拟器上验证，实时生成高保真深度图。
- 重建精度和计算效率显著优于现有图像重建方法。

## 全文

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 We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction.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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