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.

翻译：我们提出了一种用于公共交通的多视角座舱内监测数据集，包含来自四台朝向车内的摄像头和一台覆盖数字化且部分自动化德国城市公交车车厢内部的旋转激光雷达的同步RGB与深度图像。该数据集包含9136个带有标注的同步样本，并附带一个标定与伪标签生成管线，可生成乘客的3D人体姿态估计和定向3D包围框。我们进一步提供了nuScenes格式转换功能，并对代表性多视角3D检测模型（例如Lift-Splat-Shoot和BEVFusion）进行了基准测试，支持多视角座舱内感知模型的对比评估和小规模训练。该数据集及工具可在https://github.com/EvgenyGorelik/multiview_incabin_dataset获取。