DSERT-RoLL：面向多样驾驶条件的鲁棒多模态感知——立体事件-RGB-热成像相机、4D雷达与双LiDAR
DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR
为事件相机、4D雷达、热成像等新传感器提供了带2D/3D框与轨迹ID的多模态驾驶数据与统一基准，可直接用于研究跨传感器家族的融合策略与恶劣天气下的3D检测鲁棒性。
原文依据 We establish unified 3D and 2D benchmarks that enable direct comparison of characteristics and strengths across sensor families
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 DSERT-RoLL，一个驾驶数据集，集成了立体事件相机、RGB 相机与热成像相机，以及 4D 雷达和双 LiDAR，并在多样天气与光照条件下采集。该数据集提供带轨迹 ID 的精确 2D 与 3D 边界框以及自车里程计，从而支持在单一传感器组合内部与跨传感器组合之间进行公平比较。它旨在缓解事件相机与 4D 雷达等新型传感器的数据稀缺问题，并支持对其行为开展系统性研究。我们建立了统一的 3D 与 2D 基准，使得能够直接比较各传感器家族之间以及每个家族内部的特性与优势。我们报告了具有代表性的单模态与多模态方法的基线结果，并提供了鼓励研究不同融合策略与传感器组合的评测协议。此外，我们提出了一个融合框架，将各传感器特有的线索整合到统一特征空间中，并提升了在多变天气与光照条件下 3D 检测的鲁棒性。
英文原文摘要 1034 字符
In this paper, we present DSERT-RoLL, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse weather and illumination conditions. The dataset provides precise 2D and 3D bounding boxes with track IDs and ego vehicle odometry, enabling fair comparisons within and across sensor combinations. It is designed to alleviate data scarcity for novel sensors such as event cameras and 4D radar and to support systematic studies of their behavior. We establish unified 3D and 2D benchmarks that enable direct comparison of characteristics and strengths across sensor families and within each family. We report baselines for representative single modality and multimodal methods and provide protocols that encourage research on different fusion strategies and sensor combinations. In addition, we propose a fusion framework that integrates sensor specific cues into a unified feature space and improves 3D detection robustness under varied weather and lighting.