标题： 面向基于LiDAR的3D检测的铁路领域自适应：通过SynDRA-BBox实现道路到铁路和模拟到真实 显示英文标题

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

摘要： 近年来，对自动列车操作的兴趣显著增加。 为了实现高级功能，基于视觉的鲁棒算法对于感知和理解周围环境至关重要。 然而，铁路行业缺乏公开的现实世界标注数据集，这使得在该领域测试和验证新的感知解决方案变得具有挑战性。 为了解决这一差距，我们引入了 SynDRA-BBox，一个旨在支持现实铁路场景中目标检测和其他基于视觉任务的合成数据集。 据我们所知，这是第一个专门针对铁路领域2D和3D目标检测的合成数据集，该数据集可在 https://syndra.retis.santannapisa.it 公开获取。 在所呈现的评估中，一种最先进的半监督域适应方法，最初是为汽车感知开发的，被适应到铁路环境中，使合成数据能够转移到3D目标检测。 实验结果展示了有希望的性能，突显了合成数据集和域适应技术在提升铁路环境感知能力方面的有效性。 显示英文摘要

摘要： 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.