为提升骑行安全，构建了首个自行车搭载的激光雷达语义分割数据集。
BikeScenes: LiDAR Semantic Segmentation for Bicycles
- 基于自行车平台采集校园场景的3021帧激光雷达数据，标注29类物体。
- 用预训练模型微调后，平均交并比从13.8%提升至63.6%。
- 适合关注骑行者感知、自动驾驶安全或3D点云分割的研究者。
骑手的脆弱性因电动自行车普及而加剧，推动将汽车感知技术应用于骑行安全。我们利用多传感器SenseBike研究平台，开展自行车搭载固态激光雷达的3D语义分割研究。提出新型BikeScenes-lidarseg数据集，包含3021个连续激光雷达扫描帧，覆盖荷兰代尔夫特理工大学校园，对29类动态与静态物体进行语义标注。作为初步基准研究，评估了在SemanticKITTI上预训练的FRNet模型在该自行车平台上的迁移能力。在BikeScenes上微调后，对保留子序列的平均交并比（mIoU）从13.8%提升至63.6%。结果表明，领域内数据对本平台具有显著实用价值，同时也凸显了更大规模自行车数据集的必要性。我们贡献BikeScenes作为推进以骑行者为中心感知研究的资源。
原文摘要 · Abstract (English)
The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor SenseBike research platform to study 3D LiDAR semantic segmentation for bicycles. We introduce the novel BikeScenes-lidarseg Dataset, comprising 3021 consecutive LiDAR scans around the university campus of the TU Delft, semantically annotated for 29 dynamic and static classes. As an initial baseline study, we evaluate how a SemanticKITTI pre-trained FRNet model transfers to this bicycle-mounted solid-state LiDAR setting. Fine-tuning on BikeScenes increases mean Intersection-over-Union (mIoU) from 13.8% to 63.6% on our held-out subsequences. These results show the practical value of in-domain data for this platform, while also highlighting the need for larger bicycle datasets. We contribute BikeScenes as a resource for advancing research in cyclist-centric perception.
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