# OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality

arXiv: https://arxiv.org/abs/2602.22920 · HTML: https://mlsanity.com/paper/2602.22920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [OSDaR23: Open Sensor Data for Rail 2023](https://mlsanity.com/paper/2305.03001/) (2023)
- [TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation](https://mlsanity.com/paper/2302.14486/) (2023)
- [RailGoerl24: G"orlitz Rail Test Center CV Dataset 2024](https://mlsanity.com/paper/2504.00204/) (2025)
- [SynRailObs: A Synthetic Dataset for Obstacle Detection in Railway Scenarios](https://mlsanity.com/paper/2505.10784/) (2025)
- [Local and Global Information in Obstacle Detection on Railway Tracks](https://mlsanity.com/paper/2307.15478/) (2023)
- [Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox](https://mlsanity.com/paper/2507.16413/) (2025)

## Built on by (most cited first, 2 of 2)

- [Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments](https://mlsanity.com/paper/2604.14781/) (2026)
- [Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles](https://mlsanity.com/paper/2607.28483/) (2026)

## Abstract (arXiv)

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