Human-centric Scene Understanding for 3D Large-scale Scenarios
Yiteng Xu Peishan Cong Yichen Yao Runnan Chen Yuenan Hou Xinge Zhu Xuming He Jingyi Yu Yuexin Ma
Human-centric scene understanding is significant for real-world applications,
but it is extremely challenging due to the existence of diverse human poses and
actions, complex human-environment interactions, severe occlusions in crowds,
etc. In this paper, we present a large-scale multi-modal dataset for
human-centric scene understanding, dubbed HuCenLife, which is collected in
diverse daily-life scenarios with rich and fine-grained annotations. Our
HuCenLife can benefit many 3D perception tasks, such as segmentation,
detection, action recognition, etc., and we also provide benchmarks for these
tasks to facilitate related research. In addition, we design novel modules for
LiDAR-based segmentation and action recognition, which are more applicable for
large-scale human-centric scenarios and achieve state-of-the-art performance.