构建首个面向移动机器人的人类密集环境语义占位数据集
MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots
- 通过图像与激光雷达融合重建可变形人体几何
- 建立占位预测与行人速度预测双基准评测体系
- 适用于人机交互、机器人导航等场景研究
在人流密集环境中，移动机器人实现高精度三维语义占位感知至关重要，但该领域仍远未被充分探索。为此，我们提出MobileOcc，一个面向移动机器人在人群环境中的语义占位数据集。该数据集基于包含静态物体占位标注的注释流程，并引入一种专为人体占位建模设计的新颖网格优化框架：从2D图像重建可变形人体几何，再利用关联的激光雷达点云进行精炼与优化。基于MobileOcc，我们为两项任务建立了基准：1）占位预测；2）行人速度预测，涵盖单目、双目及全景占位等多种方法，提供评估指标与基线实现以支持可复现对比。此外，我们还将该注释方法应用于3D人体姿态估计数据集，结果表明其在不同数据集上均表现出鲁棒性能。代码与数据集已公开于 https://autonomousrobots.nl/paper_websites/mobileocc。
原文摘要 · Abstract (English)
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, then 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. Our code and dataset are released at https://autonomousrobots.nl/paper_websites/mobileocc
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