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.

翻译：自然灾害频发对人类生命构成严重威胁并造成重大经济损失。尽管三维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门针对灾后环境设计的数据集。为填补这一空白，我们利用无人机采集的飓风伊恩（2022年）受灾区域航拍影像，通过运动恢复结构（SfM）与多视图立体（MVS）技术重建三维点云，构建了专用三维数据集。我们在该数据集上评估了当前最先进（SOTA）的三维语义分割模型——快速点云Transformer（FPT）、点云Transformer v3（PTv3）及OA-CNNs，揭示了现有方法在灾损区域识别中的显著局限性。这些发现凸显了推进三维分割技术发展、构建专用三维基准数据集的迫切需求，以提升灾后场景理解与应急响应能力。