用可学习排序提升灾后3D点云分割效率与精度
OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
- 设计可学习点排序模块，动态生成最优点序以增强注意力局部性
- 在3DAeroRelief数据集上超越现有方法，显著提升分割准确率
- 适合需要快速精准灾损评估的应急响应与遥感分析场景
灾后损伤评估需快速准确地对三维点云进行语义分割，以识别受损建筑和道路等关键基础设施。早期点变换器（如PTv1、PTv2）依赖计算开销大的邻域搜索（k-NN）和最远点采样（FPS）。为提升效率，近期架构如Point Transformer V3（PTv3）采用希尔伯特曲线或Z-order等静态序列化方法，对无序点进行窗口注意力处理。然而，这些固定顺序难以适应灾害场景中复杂的几何结构。本文提出OPTNet（Ordering Point Transformer Network），引入可学习的点排序模块。OPTNet通过自监督排序损失，动态预测能最大化注意力局部性的最优排列。我们在3DAeroRelief数据集上评估该方法，显著优于当前最佳基线。
原文摘要 · Abstract (English)
Post-disaster damage assessment requires rapid and accurate semantic segmentation of 3D point clouds to identify critical infrastructure such as damaged buildings and roads. Early Point Transformers (e.g., PTv1, PTv2) relied on computationally expensive neighbor searching (k-NN) and Farthest Point Sampling (FPS). To improve efficiency, recent architectures like Point Transformer V3 (PTv3) adopted static serialization methods, such as Hilbert curves or Z-order, to organize unstructured points for window-based attention. However, these fixed orderings are not optimal for capturing the complex geometry of disaster scenes. In this paper, we propose OPTNet (Ordering Point Transformer Network), which introduces a learnable Point Sorter module. OPTNet utilizes a self-supervised ordering loss to dynamically predict an optimal permutation that maximizes the locality of the attention mechanism. We evaluate our method on the 3DAeroRelief dataset, significantly outperforming state-of-the-art baselines.
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