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
Abstract
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. The dataset and code can be found at https://github.com/4DVLab/HuCenLife.git.
BibTeX
@inproceedings{iccv2023_humancentricscen,
title = {Human-centric Scene Understanding for 3D Large-scale Scenarios},
author = {Yiteng Xu and Peishan Cong and Yichen Yao and Runnan Chen and Yuenan Hou and Xinge Zhu and Xuming He and Jingyi Yu and Yuexin Ma},
booktitle = {ICCV 2023},
year = {2023}
}