构建跨传感器与恶劣天气的激光雷达仿真框架与数据集
ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions
- 基于物理模型与学习模块融合，生成符合目标传感器特性的点云
- 构建了具有真实对应关系的点云数据集，支持跨域一致性测试
- 适合自动驾驶感知系统开发与仿真研究者使用
可靠的激光雷达感知需具备跨传感器、环境及恶劣天气的鲁棒性。然而现有数据集很少提供同一场景在不同传感器配置和天气条件下的物理一致观测，限制了领域偏移的系统分析。本文提出ReaLiTy，一个统一的物理引导框架，可将激光雷达数据转换为目标传感器规格和天气条件。该框架结合物理驱动线索与学习模块，生成真实的强度模式；同时引入基于物理的天气模型，实现几何与辐射退化的一致模拟。基于此框架，我们构建了激光雷达适应数据集套件（LADS），包含一组物理一致、可直接转换的点云数据，与原始数据一一对应。实验表明，该方法显著提升跨域一致性与真实天气效果。ReaLiTy与LADS为研究激光雷达适应及智能交通系统的仿真驱动感知提供了可复现基础。
原文摘要 · Abstract (English)
Reliable LiDAR perception requires robustness across sensors, environments, and adverse weather. However, existing datasets rarely provide physically consistent observations of the same scene under varying sensor configurations and weather conditions, limiting systematic analysis of domain shifts. This work presents ReaLiTy, a unified physics-informed framework that transforms LiDAR data to match target sensor specifications and weather conditions. The framework integrates physically grounded cues with a learning-based module to generate realistic intensity patterns, while a physics-based weather model introduces consistent geometric and radiometric degradations. Building on this framework, we introduce the LiDAR Adaptation Dataset Suite (LADS), a collection of physically consistent, transformation-ready point clouds with one-to-one correspondence to original datasets. Experiments demonstrate improved cross-domain consistency and realistic weather effects. ReaLiTy and LADS provide a reproducible foundation for studying LiDAR adaptation and simulation-driven perception in intelligent transportation systems.
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