构建首个铁路场景3D检测合成数据集，助力真实铁路环境感知
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
- 构建铁路专用合成数据集SynDRA-BBox，支持2D/3D目标检测
- 在真实铁路场景中实现合成数据到3D检测的跨域迁移，性能显著
- 适合研究铁路自动驾驶、视觉感知与域适应的开发者
近年来，自动驾驶列车的兴趣显著增加。为实现高级功能，鲁棒的基于视觉的算法对于感知和理解周围环境至关重要。然而，铁路领域缺乏公开可用的真实世界标注数据集，使得在该领域测试和验证新感知解决方案面临挑战。为解决这一差距，我们提出SynDRA-BBox，一个专为真实铁路场景中的目标检测及其他视觉任务设计的合成数据集。据我们所知，这是首个专门针对铁路领域的2D和3D目标检测合成数据集，数据集公开获取地址为https://syndra.retis.santannapisa.it。在实验评估中，我们将原本用于汽车感知的先进半监督域适应方法应用于铁路场景，实现了合成数据向3D目标检测的可迁移性。实验结果表明性能优异，凸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。
原文摘要 · Abstract (English)
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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