MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots
This addresses a gap for mobile robotics in pedestrian-rich settings, though it is incremental as it adapts existing techniques to a new domain.
The authors tackled the lack of dense 3D semantic occupancy datasets for mobile robots in crowded human environments by introducing MobileOcc, a dataset built with a novel annotation pipeline that includes human occupancy modeling and mesh optimization. They established benchmarks for occupancy and pedestrian velocity prediction, showing robust performance across datasets.
Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored compared to its application in autonomous driving. To address this gap, we present MobileOcc, a semantic occupancy dataset for mobile robots operating in crowded human environments. Our dataset is built using an annotation pipeline that incorporates static object occupancy annotations and a novel mesh optimization framework explicitly designed for human occupancy modeling. It reconstructs deformable human geometry from 2D images and subsequently refines and optimizes it using associated LiDAR point data. Using MobileOcc, we establish benchmarks for two tasks, i) Occupancy prediction and ii) Pedestrian velocity prediction, using different methods including monocular, stereo, and panoptic occupancy, with metrics and baseline implementations for reproducible comparison. Beyond occupancy prediction, we further assess our annotation method on 3D human pose estimation datasets. Results demonstrate that our method exhibits robust performance across different datasets.