发表机构
Western Australia Machine Intelligence Group Pty Ltd; The University of Western Australia; Curtin University（西澳大利亚机器智能集团有限公司; 西澳大利亚大学; 科廷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自动驾驶等领域的目标级变化检测问题，提出LoDA检测级别感知方法，构建LoDA基准，在Subiaco和Urb3DCD-V2基准上均超越现有最佳基线，提升了变化检测性能。
AI 中文摘要
高清3D激光雷达（LiDAR）地图对自动驾驶和智慧城市服务至关重要，这类服务需要可靠检测多时相城市LiDAR中的目标级变化，以保持数字地图与物理世界的一致性。现有方法从栅格高度差分到深度图像和点云网络，通常仍基于瓦片且受阈值驱动，生成逐点分数却无明确检测限或一致的目标级标签。我们提出一种目标级3D变化检测流水线，整合了检测限感知配准、几何驱动的目标代理与基于规则的语义和实例分割，以及高度、体积和表面法向方向的位移线索，以分配带有置信度的五类变化标签。通过将配准、几何和语义解耦，该流水线将位姿不确定性传播到空间变化的检测限中，稳定跨历元对应关系，并抑制由残余配准误差和密度变化导致的虚假变化。我们还提出了LoDA，一种面向Subiaco区的检测级别（Level of Detection, LoD）感知基准，该基准由LiDAR、GNSS和IMU支持构建的多时相车载LiDAR融合地图、语义实例和目标级注释组成。在该基准上，我们的方法达到95.0%的准确率、90.8%的宏F1值和83.0%的宏交并比（IoU），比最佳基线分别高出8.7个IoU点和4.4个F1点。在公共Urb3DCD-V2基准上，按官方逐点协议评估时，该方法达到96.81%的平均准确率和89.52%的平均变化交并比（mIoUch），比已报告的最强基线分别提升了1.36个mAcc点和3.18个mIoUch点。
英文摘要
High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in multi-temporal urban LiDAR to keep digital maps aligned with the physical world. Existing approaches from raster height differencing to depth image and point-cloud networks often remain tile-based and threshold-driven, yielding per-point scores without explicit detection limits or consistent object-level labels. We propose an object-level 3D change-detection pipeline that integrates detection-limit-aware registration, geometry-driven object proxies with rule-based semantic and instance segmentation, and displacement cues in height, volume, and surface-normal direction to assign five change labels with confidence. By decoupling registration, geometry, and semantics, the pipeline propagates pose uncertainty into spatially varying detection limits, stabilizes cross-epoch correspondences, and suppresses false changes caused by residual misalignment and density variation. We also present LoDA, a level-of-detection (LoD) aware benchmark for the Subiaco district with fused multi-temporal vehicle-LiDAR maps constructed with LiDAR, GNSS, and IMU support, semantic instances, and object-level annotations. On this benchmark, our method achieves 95.0% accuracy, 90.8% macro F1, and 83.0% macro IoU, exceeding the best baseline by 8.7 IoU points and 4.4 F1 points. On the public Urb3DCD-V2 benchmark evaluated under the official point-wise protocol, it reaches 96.81% mean accuracy and 89.52% mean change IoU, improving over the strongest reported baselines by 1.36 points in mAcc and 3.18 points in mIoUch.
