发表机构
Mines Paris, PSL University; Paris-Saclay University; CEA（巴黎高等矿业学校（PSL大学）; 巴黎萨克雷大学; 法国原子能和替代能源委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结构化评估协议，从粗标签、恶劣环境干扰、域泛化及嵌入式平台推理速度维度评估LiDAR语义分割模型的部署就绪性，发现基准性能与实车部署需求存在差距，为相关评估提供参考。
AI 中文摘要
基于激光雷达（LiDAR）的语义分割是自动驾驶车辆与移动机器人的核心感知模块。尽管近期最先进的方法在标准基准上表现强劲，但现有评估协议仍聚焦于干净的单域设置与细粒度标签分类，基本未评估部署就绪性。实车系统必须处理关乎安全的标签语义、退化的感知条件及跨域变异性，但目前尚无统一协议同时覆盖这三个方面。本文提出一种结构化评估协议，沿三个互补维度评估LiDAR语义分割模型的部署就绪性：（i）契合自动驾驶安全优先级的粗标签评估，揭示标签粒度对不同方法的影响；（ii）八种模拟真实大气、几何及传感器退化的LiDAR干扰下的鲁棒性；（iii）无适配情况下跨数据集的域泛化。评估包含在嵌入式Jetson AGX Orin平台上测得的推理速度，直接反映部署约束。结果表明，细粒度基准排名未必反映安全相关性能，所有方法在干扰下均出现显著退化且鲁棒性具架构依赖性，当前域泛化仍不足以支持可靠部署。这些发现暴露了基准性能与部署就绪性间的具体差距，为更贴合实际的LiDAR语义分割评估提供参考协议。
英文摘要
LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots. Despite the strong performance of recent state-of-the-art methods on standard benchmarks, existing evaluation protocols remain focused on clean, single-domain settings and fine-grained label taxonomies, leaving deployment readiness largely unassessed. Real-world systems must handle safety-critical label semantics, degraded sensing conditions, and cross-domain variability, yet no unified protocol currently addresses all three aspects together. In this paper, we propose a structured evaluation protocol that assesses the deployment readiness of LiDAR semantic segmentation models along three complementary dimensions: (i) coarse-label evaluation aligned with autonomous driving safety priorities, revealing how label granularity affects different methods; (ii) robustness under eight types of LiDAR corruptions designed to emulate real-world atmospheric, geometric, and sensor degradations; and (iii) domain generalization across datasets without adaptation. The evaluation includes inference speed measured on an embedded Jetson AGX Orin platform, directly reflecting deployment constraints. Our results show that fine-grained benchmark rankings do not always reflect safety-relevant performance, that all methods experience substantial degradation under corruptions with architecture-dependent robustness characteristics, and that current domain generalization remains insufficient for reliable deployment. These findings expose concrete gaps between benchmark performance and deployment readiness, and provide a reference protocol for more practically grounded evaluation of LiDAR semantic segmentation.
