几何与结构：基于图的LiDAR点云仿真保真度诊断

中文摘要 AI 辅助

数字孪生为验证自动驾驶和高级驾驶辅助系统（ADAS）的传感器流水线提供了一种可扩展且成本效益高的真实世界测试补充方案。然而，量化其保真度仍然具有挑战性，尤其是对于3D LiDAR点云，传统几何度量可能忽略重要的结构差异。我们提出了一种基于图的框架，用于评估模拟LiDAR点云相对于真实世界扫描的结构保真度。虽然扫描级度量（如Chamfer距离）捕捉了点级几何相似性，但它们并未明确表示连通性、拓扑或对象级组织。我们的框架从真实和模拟点云构建图，应用Louvain社区检测来识别空间连贯的子图，并使用质心邻近性匹配对应社区。对于每个匹配对，我们计算$r_\lambda$，一种受Weyl不等式启发的有界图谱度量，并将其与密度感知Chamfer距离（CDC）作为几何基线进行比较。受控扰动实验表明，$r_\lambda$对刚性变换具有不变性，对传感器噪声具有鲁棒性，同时对结构变形保持敏感。我们在分别使用Velodyne VLP-32C传感器和CARLA获取的50对配对真实和模拟LiDAR扫描上评估了该框架。数据集包含超过1000个匹配社区，涵盖四类代表性类别：车辆、植被、树木和建筑墙壁。结果表明，几何和结构度量捕捉了模拟保真度的互补方面，支持图谱分析作为验证ADAS和自动驾驶应用中数字孪生的额外诊断层。

英文摘要

Digital twins provide a scalable and cost-effective complement to real-world testing for validating autonomous-driving and advanced driver-assistance system (ADAS) sensor pipelines. However, quantifying their fidelity remains challenging, particularly for 3D LiDAR point clouds, where conventional geometric metrics may overlook important structural discrepancies. We present a graph-based framework for evaluating the structural fidelity of simulated LiDAR point clouds against real-world scans. While scan-level metrics such as Chamfer distance capture point-wise geometric similarity, they do not explicitly represent connectivity, topology, or object-level organization. Our framework constructs graphs from real and simulated point clouds, applies Louvain community detection to identify spatially coherent subgraphs, and matches corresponding communities using centroid proximity. For each matched pair, we compute $r_λ$, a bounded graph-spectral metric motivated by Weyl's inequality, and compare it with density-aware Chamfer distance (CDC) as a geometric baseline. Controlled perturbation experiments demonstrate that $r_λ$ is invariant to rigid transformations and robust to sensor noise while remaining sensitive to structural deformation. We evaluate the framework on 50 paired real and simulated LiDAR scans acquired using a Velodyne VLP-32C sensor and CARLA, respectively. The dataset contains more than 1,000 matched communities across four representative classes: vehicles, vegetation, trees, and building walls. The results show that geometric and structural measures capture complementary aspects of simulation fidelity, supporting graph-spectral analysis as an additional diagnostic layer for validating digital twins in ADAS and autonomous-driving applications.

发表机构

• National Research Council Canada（加拿大国家研究委员会）

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