AI RESEARCH
OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
arXiv CS.LG
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ArXi:2605.17197v1 Announce Type: new 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.