基于激光雷达的城市场景变化检测，提升精度与内存效率。
LiDAR-based 3D Change Detection at City Scale
- 用多分辨率NDT与点面ICP对齐点云，结合表面粗糙度和协方差计算检测置信度。
- 在西澳苏比阿科市测试中，准确率达95.3%，宏平均F1为90.8%。
- 适合城市规划、资产监控等需要高精度变化识别的应用场景。
高精度三维城市地图支持城市规划与变化检测，对市政合规、地图维护及资产监测（包括建筑与城市绿化）至关重要。传统数字地表模型（DSM）与图像差分易受垂直偏差和视角不匹配影响；原始点云或体素模型则需大量内存，假设对齐完美且易丢失细结构。本文提出一种不确定性感知、以物体为中心的城市级激光雷达变化检测方法。通过多分辨率法向分布变换（NDT）与点面对齐迭代最近点（ICP）对齐不同时期数据，归一化高程，并基于配准协方差与表面粗糙度计算逐点检测置信度，校准变化判断。几何关联通过语义与实例分割优化，采用带增强虚节点的类别约束二分图分配处理分裂合并情况。分块处理控制内存并保留窄结构变化，实例级决策在局部检测门控下整合重叠、位移与体积差异。在澳大利亚西澳州苏比阿科市2023年与2025年采集的数据集上实验，本方法达到95.3%准确率、90.8%宏平均F1、82.9%宏平均交并比，优于最强基线模型Triplet KPConv，分别提升0.3、0.6、1.1个百分点。
原文摘要 · Abstract (English)
High-definition 3D city maps enable city planning and change detection, which is essential for municipal compliance, map maintenance, and asset monitoring, including both built structures and urban greenery. Conventional Digital Surface Model (DSM) and image differencing are sensitive to vertical bias and viewpoint mismatch, while original point cloud or voxel models require large memory, assume perfect alignment, and degrade thin structures. We propose an uncertainty-aware, object-centric method for city-scale LiDAR-based change detection. Our method aligns data from different time periods using multi-resolution Normal Distributions Transform (NDT) and a point-to-plane Iterative Closest Point (ICP) method, normalizes elevation, and computes a per-point level of detection from registration covariance and surface roughness to calibrate change decisions. Geometry-based associations are refined by semantic and instance segmentation and optimized using class-constrained bipartite assignment with augmented dummies to handle split-merge cases. Tiled processing bounds memory and preserves narrow ground changes, while instance-level decisions integrate overlap, displacement, and volumetric differences under local detection gating. We perform experiments on the city of Subiaco, Western Australia, using datasets captured in 2023 and 2025. Our method achieves 95.3% accuracy, 90.8% macro F1, and 82.9% macro IoU, improving over the strongest baseline, Triplet KPConv, by 0.3, 0.6, and 1.1 percentage points, respectively.
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