MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots

Dense 3D semantic occupancy perception is critical for mo-bile robots operating in pedestrian-rich environments, yet it remains un-derexplored compared to its application in autonomous driving. To ad-dress this gap, we present MobileOcc, a semantic occupancy dataset formobile robots operating in crowded human environments. Our dataset isbuilt using an annotation pipeline that incorporates static object occu-pancy annotations and a novel mesh optimization framework explicitlydesigned for human occupancy modeling. It reconstructs deformable hu-man geometry from 2D images, then refines and optimizes it using as-sociated LiDAR point data. Using MobileOcc, we establish benchmarksfor two tasks: i) Occupancy prediction and ii) Pedestrian velocity predic-tion, using different methods, including monocular, stereo, and panopticoccupancy, with metrics and baseline implementations for reproduciblecomparison. Beyond occupancy prediction, we further assess our annota-tion method on 3D human pose estimation datasets. Results demonstratethat our method exhibits robust performance across different datasets.Our code and dataset are released at https://autonomousrobots.nl/paper_websites/mobileocc