Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox

• URL: http://arxiv.org/abs/2507.16413v1
• Date: Tue, 22 Jul 2025 10:04:49 GMT
• Title: Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
• Authors: Xavier Diaz, Gianluca D'Amico, Raul Dominguez-Sanchez, Federico Nesti, Max Ronecker, Giorgio Buttazzo,
• Abstract summary: We introduce SynDRA-BBox, a synthetic dataset designed to support object detection and other vision-based tasks in realistic railway scenarios.<n>To the best of our knowledge, is the first synthetic dataset specifically tailored for 2D and 3D object detection in the railway domain.<n>A state-of-the-art semi-supervised domain adaptation method is adapted to the railway context, enabling the transferability of synthetic data to 3D object detection.
• Score: 3.3810628880631226
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving and understanding the surrounding environment. However, the railway sector suffers from a lack of publicly available real-world annotated datasets, making it challenging to test and validate new perception solutions in this domain. To address this gap, we introduce SynDRA-BBox, a synthetic dataset designed to support object detection and other vision-based tasks in realistic railway scenarios. To the best of our knowledge, is the first synthetic dataset specifically tailored for 2D and 3D object detection in the railway domain, the dataset is publicly available at https://syndra.retis.santannapisa.it. In the presented evaluation, a state-of-the-art semi-supervised domain adaptation method, originally developed for automotive perception, is adapted to the railway context, enabling the transferability of synthetic data to 3D object detection. Experimental results demonstrate promising performance, highlighting the effectiveness of synthetic datasets and domain adaptation techniques in advancing perception capabilities for railway environments.

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