Abstract
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.
Abstract (translated)
自然灾害频发的增加给人类生命带来了严重的威胁，并导致了巨大的经济损失。虽然三维语义分割对于灾后的评估至关重要，但现有的深度学习模型缺乏专门为灾后环境设计的数据集。为了填补这一空白，我们利用无人飞行器（UAV）拍摄的2022年飓风伊安过境区域的空中视频构建了一个专门的3D数据集，并采用基于运动的结构重建（Structure-from-Motion, SfM）和多视角立体视觉（Multi-View Stereo, MVS）技术来重构3D点云。我们在这个数据集上评估了最先进的三维语义分割模型，包括Fast Point Transformer (FPT)，Point Transformer v3 (PTv3) 和 OA-CNNs，揭示了现有方法在受灾地区应用中的显著局限性。这些发现强调了改进三维分割技术以及开发专门的三维基准数据集以提高灾后场景理解和响应的紧迫需求。
URL
https://arxiv.org/abs/2512.24593
