从单个激光雷达扫描数据重新思考三维分割：SIP基准上的入射角感知采样
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark
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中文总结 AI 辅助
针对单个激光雷达扫描的三维分割，本研究在SIP基准上提出入射角感知采样策略，提升了非平面构件等的分割性能，降低了对采样分辨率的敏感性。
中文摘要 AI 辅助
三维场景理解在建筑领域愈发重要，但多数方法是在精心整理的数据集上开发的，无法完全反映真实工地的感知条件。在诸多工作流程中，单个激光雷达扫描提供的是快速局部更新，而非完整的场景表示，这会产生有限的表面覆盖范围、由采集驱动的密度变化，以及主导平面与稀疏建筑构件间的严重不平衡。由于必须对大型点云进行下采样，采样分辨率和点分配直接影响几何细节与空间上下文之间的平衡。本研究在固定的每片段点预算下评估这些影响，并为单个激光雷达扫描引入一种入射角感知采样策略。该方法将点映射到几何归一化流形空间以进行基于体素的选择，同时保留原始欧氏坐标供下游学习。它仅需点坐标和法向量，无需修改主干网络。在Site in Pieces（SIP）基准上，使用Point Transformer和PointNeXt的实验表明，该方法提升了分辨率平均分割性能，尤其针对非平面构件和梯子，同时降低了对采样分辨率的敏感性。结果显示，感知采集的采样可提供更稳定的几何表示，应被视为单个扫描三维分割的活跃组件，而非通用预处理步骤。
英文摘要
3D scene understanding is increasingly important in construction, yet most methods are developed on curated datasets that do not fully reflect real site sensing conditions. In many workflows, individual LiDAR scans provide rapid local updates rather than complete scene representations, producing limited surface coverage, acquisition-driven density variation, and severe imbalance between dominant planar surfaces and sparse construction elements. Because large point clouds must be downsampled, sampling resolution and point allocation directly affect the balance between geometric detail and spatial context. This study evaluates these effects under a fixed per-fragment point budget and introduces an incidence-aware sampling strategy for individual LiDAR scans. The method maps points to a geometry-normalized manifold space for voxel-based selection while preserving original Euclidean coordinates for downstream learning. It requires only point coordinates and normals and no backbone modification. Using the Site in Pieces (SIP) benchmark, experiments with Point Transformer and PointNeXt show improved resolution-averaged segmentation performance, especially for non-planar elements and ladders, while reducing sensitivity to sampling resolution. The results show that acquisition-aware sampling can provide a more stable geometric representation and should be treated as an active component of individual-scan 3D segmentation rather than generic preprocessing.
