构建公交车内多视角监控数据集，支持3D人体姿态与位置检测。
Multi-View In-Cabin Monitoring System for Public Transport Vehicles
- 四路摄像头+旋转LiDAR同步采集车内多视角图像与深度数据。
- 含9136个标注样本，生成3D人体姿态与定向边界框伪标签。
- 提供nuScenes格式转换与基准模型，适合交通场景感知研究。
我们提出一个用于公共交通的多视角车内监控数据集，包含来自四路朝内摄像头的同步RGB与深度图像，以及覆盖车辆内部的旋转LiDAR数据，基于数字化且部分自动化的德国城市巴士。该数据集包含9,136个同步样本并附带标注，同时提供校准与伪标签生成流程，可生成乘客的3D人体姿态估计和方向化3D边界框。此外，还提供nuScenes格式转换及代表性多视角3D检测模型（如Lift-Splat-Shoot与BEVFusion），支持多视角车内感知模型的对比评估与小规模训练。数据集与工具已公开于https://github.com/EvgenyGorelik/multiview_incabin_dataset。
原文摘要 · Abstract (English)
We introduce a multi-view in-cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward-facing cameras and a rotating LiDAR covering the vehicle interior of a digitalized and partly automated German city bus. The dataset contains 9.136 synchronized samples with annotations and is accompanied by a calibration and pseudo-labeling pipeline that generates 3D human pose estimates and oriented 3D bounding boxes for occupants. We further provide a nuScenes-format conversion and benchmark representative multi-view 3D detection models (e.g., Lift-Splat-Shoot and BEVFusion), supporting comparative evaluation and small-scale training of multi-view in-cabin perception models. The dataset and tools are available at https://github.com/EvgenyGorelik/multiview_incabin_dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。