针对灾后场景构建3D语义分割数据集，揭示现有模型短板。
3D Semantic Segmentation for Post-Disaster Assessment
- 用无人机航拍+三维重建技术构建灾后3D点云数据集
- 在飓风艾伦受灾区测试发现主流模型分割准确率显著下降
- 为灾后评估提供专用数据与评测基准，适合应急响应研究者
自然灾害频发威胁生命安全并造成巨大经济损失。3D语义分割对灾后评估至关重要，但现有深度学习模型缺乏专为灾后环境设计的数据集。为此，我们利用无人机拍摄的飓风艾伦（2022）受灾区域航拍影像，通过运动恢复结构（SfM）和多视角立体匹配（MVS）技术重建3D点云，构建了专用3D数据集。我们在该数据集上评估了当前最优的3D语义分割模型：Fast Point Transformer（FPT）、Point Transformer v3（PTv3）和OA-CNNs，发现这些方法在灾后区域存在显著性能瓶颈。研究凸显了改进3D分割技术的紧迫性，并强调需开发专用3D基准数据集以提升灾后场景理解与应急响应能力。
原文摘要 · Abstract (English)
The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-disaster environments. To address this gap, we constructed a specialized 3D dataset using unmanned aerial vehicles (UAVs)-captured aerial footage of Hurricane Ian (2022) over affected areas, employing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques to reconstruct 3D point clouds. We evaluated the state-of-the-art (SOTA) 3D semantic segmentation models, Fast Point Transformer (FPT), Point Transformer v3 (PTv3), and OA-CNNs on this dataset, exposing significant limitations in existing methods for disaster-stricken regions. These findings underscore the urgent need for advancements in 3D segmentation techniques and the development of specialized 3D benchmark datasets to improve post-disaster scene understanding and response.
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