用增强现实技术生成真实铁路场景的高质量标注数据，解决感知模型训练数据少的问题。
OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality
- 融合激光雷达与定位数据，在真实铁路视频中精准放置虚拟物体。
- 通过分割优化定位数据，使增强画面在时空上更真实稳定。
- 构建公开数据集OSDaR-AR，支持下一代铁路感知系统研发。
尽管深度学习显著提升了智能交通系统的感知能力，铁路应用仍面临障碍检测等关键任务中高质量标注数据稀缺的问题。虽然逼真仿真器可提供解决方案，但普遍存在‘仿真到现实’的差距；而简单的图像掩码方法则缺乏生成单帧与多帧场景所需的时空一致性。本文提出一种多模态增强现实框架，将逼真虚拟物体整合至OSDaR23数据集的真实铁路序列中。利用Unreal Engine 5特性，结合激光雷达点云与INS/GNSS数据，确保虚拟物体在RGB帧间的准确位置与时间稳定性。同时提出基于分割的INS/GNSS数据优化策略，经实验验证显著提升增强序列的真实感。精心设计的增强序列构成公开数据集OSDaR-AR，旨在支持下一代铁路感知系统的发展。数据集获取地址：https://syndra.retis.santannapisa.it/osdarar.html
原文摘要 · Abstract (English)
Although deep learning has significantly advanced the perception capabilities of intelligent transportation systems, railway applications continue to suffer from a scarcity of high-quality, annotated data for safety-critical tasks like obstacle detection. While photorealistic simulators offer a solution, they often struggle with the ``sim-to-real" gap; conversely, simple image-masking techniques lack the spatio-temporal coherence required to obtain augmented single- and multi-frame scenes with the correct appearance and dimensions. This paper introduces a multi-modal augmented reality framework designed to bridge this gap by integrating photorealistic virtual objects into real-world railway sequences from the OSDaR23 dataset. Utilizing Unreal Engine 5 features, our pipeline leverages LiDAR point-clouds and INS/GNSS data to ensure accurate object placement and temporal stability across RGB frames. This paper also proposes a segmentation-based refinement strategy for INS/GNSS data to significantly improve the realism of the augmented sequences, as confirmed by the comparative study presented in the paper. Carefully designed augmented sequences are collected to produce OSDaR-AR, a public dataset designed to support the development of next-generation railway perception systems. The dataset is available at the following page: https://syndra.retis.santannapisa.it/osdarar.html
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