代表性论文 Selected Papers
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Yudi Dai, Yitai Lin, Chenglu Wen*, Siqi Shen, Lan Xu, Jingyi Yu, Yuexi Ma, Cheng Wang
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR
CVPR, DOI：10.48550/arXiv.2203.09215, 2022
bibtex
略
Jialian Li, Jingyi Zhang, Zhiyong Wang, Siqi Shen, Chenglu Wen, Yuexi Ma, Lan Xu, JingYi Yu, Cheng Wang*
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
CVPR
bibtex
略
Shangbin Wu, Xu Yan. Xiaoliang Fan*, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang
Multi-Graph Fusion Networks for Urban Region Embedding
International Joint Conference on Artificial Intelligence (IJCAI-22)
@article{wu2022multi,
title={Multi-Graph Fusion Networks for Urban Region Embedding},
author={Wu, Shangbin and Yan, Xu and Fan, Xiaoliang and Pan, Shirui and Zhu, Shichao and Zheng, Chuanpan and Cheng, Ming and Wang, Cheng},
journal={arXiv preprint arXiv:2201.09760},
year={2022}
}
Weiquan Liu, Hanyun Guo, Weini Zhang, Yu Zang*, Cheng Wang, Jonathan Li
TopoSeg: Topology-aware Segmentation for Point Clouds
International Joint Conferences on Artificial Intelligence Organization (IJCAI)
@inproceedings{ijcai2022-168,
title = {TopoSeg: Topology-aware Segmentation for Point Clouds},
author = {Liu, Weiquan and Guo, Hanyun and Zhang, Weini and Zang, Yu and Wang, Cheng and Li, Jonathan},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {1201--1208},
year = {2022},
month = {7},
note = {Main Track}
doi = {10.24963/ijcai.2022/168},
url = {https://doi.org/10.24963/ijcai.2022/168},
}
Siqi Shen, Mengwei Qiu, Jun Liu, Weiquan Liu, Yongquan Fu*, Xinwang Liu, Cheng Wang
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization
NeurIPS 2022, CCF A
bibtex
@inproceedings{ResQ,
author = {Siqi Shen and
Mengwei Qiu and
Jun Liu and
Weiquan Liu and Yongquan Fu and Xinwang Liu and Cheng Wang},
title = {ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization},
booktitle = {{NeurIPS}},
year = {2022}
}
Yongquan Fu, Lun An, Kai Chen, Pere Barlet-Ros, Siqi Shen
Jellyfish: Locality-sensitive Subflow Sketching
INFOCOM 2021, CCF A
312312
321312
W Liu, B Lai, C Wang*, X Bian, C Wen, M Cheng, Y Zang, Y Xia, J Li
Matching 2D Image Patches and 3D Point Cloud Volumes by Learning Local Cross-domain Feature Descriptors
2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
bibtex
@inproceedings{liu2021matching,
title={Matching 2D Image Patches and 3D Point Cloud Volumes by Learning Local Cross-domain Feature Descriptors},
author={Liu, Weiquan and Lai, Baiqi and Wang, Cheng and Bian, Xuesheng and Wen, Chenglu and Cheng, Ming and Zang, Yu and Xia, Yan and Li, Jonathan},
booktitle={2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
pages={516--517},
year={2021},
organization={IEEE}
}
Chenglu Wen, Jinbin Tan, Fashuai Li, Chongrong Wu, Yitai Lin, Zhiyong Wang, Cheng Wang
Cooperative indoor 3D mapping and modeling using LiDAR data
Information Sciences, 574:192- 209, 2021
bibtex
@article{WEN2021192,
title = {Cooperative indoor 3D mapping and modeling using LiDAR data},
journal = {Information Sciences},
volume = {574},
pages = {192-209},
year = {2021},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2021.06.006},
url = {https://www.sciencedirect.com/science/article/pii/S0020025521005934},
author = {Chenglu Wen and Jinbin Tan and Fashuai Li and Chongrong Wu and Yitai Lin and Zhiyong Wang and Cheng Wang},
keywords = {Frame-level semantic labeling, Line model, Point-cloud-based mapping},
abstract = {Point clouds and models with semantic information facilitate various indoor automation, ranging from indoor robotics to emergency responses. Studies are currently being conducted on semantic labeling and modeling based on offline mapped point clouds, in which, the performance is strongly limited by the mapping process. To address this issue, we propose a framework to cooperatively perform the three tasks of semantic labeling, mapping, and 3D modeling of point clouds. First, our framework uses a deep-learning-assisted method to perform frame-level point cloud semantic labeling. Subsequently, point cloud frames with semantic labels are used to extract the structural planes of buildings, followed by the generation of line structures from the planes. Then, these frames are used to estimate the initial poses of a 3D sensor for data collection. In the subsequent pose optimization process, the initial poses are optimized under the constraints of the structural planes. Finally, the optimized poses are used to integrate semantic frames and line structures to generate a point cloud map and 3D line model of buildings. The experimental results show that the proposed method achieves better results than the state-of-the-art methods that separately perform one of the two tasks.}
}
