# OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation Source: [OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-05-20T05:00:13.583Z Authors: Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar Published: 2026-05-16T23:53:25.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Semantic Segmentation on 3DAeroRelief 6 (test)](): 3D Semantic Segmentation; 3DAeroRelief 6 (test); mAcc: 92.14. - [3D Semantic Segmentation on 3DAeroRelief (test)](): 3D Semantic Segmentation; 3DAeroRelief (test); mIoU: 79.65. ## Original sources and implementation [Original paper]() [PDF]()