论文
2024
Weijie Huang, Pufan Zou, Yan Xia, Chenglu Wen*, Yu Zang, Cheng Wang
OPOCA:One Point One Class Annotation for LiDAR Point Cloud Semantic Segmentation
IEEE Transactions on Geoscience and Remote Sensing
@article{huang2024opoca, title={OPOCA: One Point One Class Annotation for LiDAR Point Cloud Semantic Segmentation}, author={Huang, Weijie and Zou, Pufan and Xia, Yan and Wen, Chenglu and Zang, Yu and Wang, Cheng and Zhou, Guoqing}, journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2024}, publisher={IEEE} }
Zihui Wang#, Peizhen Yang#, Xu Yan, Xiaoliang Fan*, Xu Yan, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang, Rongshan Yu.
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Neural Networks
Neural Networks (2024): 106151.
Xun Huang, Hai Wu, Chenglu Wen*, Xiaoliang Fan, Xin Li, Cheng Wang
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection
AAAI 2024
Jinyi Zhang, Qihong Mao, Siqi Shen, Guosheng Hu, Cheng Wang
Neighborhood-enhanced 3D Human Pose Estimation with Monocular LiDAR in Long-range Outdoor Scenes
AAAI 2024, Oral, CCF A
todo
Kezheng Xiong, Maoji Zheng, Qingshan Xu, Chenglu Wen*, Siqi Shen*, Cheng Wang
SPEAL: Skeletal-Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
AAAI 2024, CCF A
todo
2023
Xiuhong Lin, Changjie Qiu, Zhipeng Cai, Siqi Shen*, Yu Zang, Weiquan Liu, Xuesheng Bian, Matthias Müller, Cheng Wang
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
NeurIPS 2023, CCF A
{}
Siqi Shen, Chennan Ma, Chao Li, Weiquan Liu, Yongquan Fu*, Songzhu Mei, Xinwang Liu, Cheng Wang
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
NeurIPS 2023, CCF A
{}
Qiming Xia, Jinhao Deng, Chenglu Wen*, Shaoshuai Shi, Xin Li, Cheng Wang
CoIn: Contrastive Instance Feature Mining for 3D Object Detection with Very Limited Annotations
ICCV 2023
Chuanpan Zheng, Xiaoliang Fan*, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
IEEE Transactions on Knowledge and Data Engineering
@article{zheng2023spatio, title={Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting}, author={Zheng, Chuanpan and Fan, Xiaoliang and Pan, Shirui and Jin, Haibing and Peng, Zhaopeng and Wu, Zonghan and Wang, Cheng and Philip, S Yu}, journal={IEEE Transactions on Knowledge \& Data Engineering}, number={01}, pages={1--14}, year={2023}, publisher={IEEE Computer Society} }
Junhao Zhao, Weijie Huang, Hai Wu, Chenglu Wen*, Bo Yang, Yulan Guo, Cheng Wang
SemanticFlow: Semantic Segmentation of Sequential LiDAR Point Clouds from Sparse Frame Annotations
IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2023.3264102
@ARTICLE{10091688, author={Zhao, Junhao and Huang, Weijie and Wu, Hai and Wen, Chenglu and Yang, Bo and Guo, Yulan and Wang, Cheng}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={SemanticFlow: Semantic Segmentation of Sequential LiDAR Point Clouds From Sparse Frame Annotations}, year={2023}, volume={61}, number={}, pages={1-11}, doi={10.1109/TGRS.2023.3264102}}
Ruijie Xiao, Chuan Zhong, Wankang Zeng, Ming Cheng, and Cheng Wang
Novel Convolutions for Semantic Segmentation of Remote Sensing Images
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
@article{xiao2023novel, title={Novel Convolutions for Semantic Segmentation of Remote Sensing Images}, author={Xiao, Ruijie and Zhong, Chuan and Zeng, Wankang and Cheng, Ming and Wang, Cheng}, journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2023}, publisher={IEEE} }
Zhimin Yuan, Ming Cheng*, Wankang Zeng, Yanfei Su, Weiquan Liu, Shangshu Yu, and Cheng Wang
Prototype-Guided Multitask Adversarial Network for Cross-Domain LiDAR Point Clouds Semantic Segmentation
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
@article{yuan2023prototype, title={Prototype-guided Multi-task Adversarial Network for Cross-domain LiDAR Point Clouds Semantic Segmentation}, author={Yuan, Zhimin and Cheng, Ming and Zeng, Wankang and Su, Yanfei and Liu, Weiquan and Yu, Shangshu and Wang, Cheng}, journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2023}, publisher={IEEE} }
Zhimin Yuan, Chenglu Wen, Ming Cheng*, Yanfei Su, Weiquan Liu, Shangshu Yu, and Cheng Wang
Category-Level Adversaries for Outdoor LiDAR Point Clouds Cross-Domain Semantic Segmentation
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
@article{yuan2022category, title={Category-Level Adversaries for Outdoor LiDAR Point Clouds Cross-Domain Semantic Segmentation}, author={Yuan, Zhimin and Wen, Chenglu and Cheng, Ming and Su, Yanfei and Liu, Weiquan and Yu, Shangshu and Wang, Cheng}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2023}, publisher={IEEE} }
Hai Wu, Chenglu Wen*, Shaoshuai Shi, Xin Li, Cheng Wang
Virtual Sparse Convolution for Multimodal 3D Object Detection
CVPR 2023
@inproceedings{VirConv, title={Virtual Sparse Convolution for Multimodal 3D Object Detection}, author={Wu, Hai and Wen,Chenglu and Shi, Shaoshuai and Wang, Cheng}, booktitle={CVPR}, year={2023} }
Wen Li, Shangshu Yu, Cheng Wang, Guosheng Hu, Siqi Shen, Chenglu Wen
SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization
CVPR 2023
暂无
Ming Yan, Xin Wang, Yudi Dai, Siqi Shen*, Chenglu Wen, Lan Xu, Yuexin Ma, Cheng Wang
CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions
CVPR 2023
@inproceedings{yan2023cimi4d,     title     = {CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions},     author    = {Yan, Ming and Wang, Xin and Dai, Yudi and Shen, Siqi and Wen, Chenglu and Xu, Lan and Ma, Yuexin and Wang, Cheng},     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},     month     = {June},     year      = {2023} }
Yudi Dai, YiTai Lin, XiPing Lin, Chenglu Wen*, Lan Xu, Hongwei Yi, Siqi Shen, Yuexin Ma, Cheng Wang
SLOPER4D: A Scene-Aware Dataset For Global 4D Human Pose Estimation In Urban Environments
CVPR 2023
@inproceedings{dai2023sloper4d, title = {SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments}, author = {Dai, Yudi and Lin, YiTai and Lin, XiPing and Wen, Chenglu and Xu, Lan and Yi, Hongwei and Shen, Siqi and Ma, Yuexin and Wang, Cheng}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023} }
Ming Yan, Xin Wang, Yudi Dai, Siqi Shen*, Chenglu Wen, Lan Xu, Yuexin Ma, Cheng Wang
CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions
CVPR 2023, CCF A
xx
Wen Li, Shangshu Yu, Cheng Wang, Guosheng Hu, Siqi Shen, Chenglu Wen
SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization
CVPR 2023, CCF A
xx
Yongquan Fu, Lun An, Siqi Shen*, Kai Chen, Pere Barlet-Ros
A One-pass Clustering based Sketch Method for Network Monitoring
IEEE/ACM Transactions on Networking (ToN), CCF A, 2023
xx
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang*, Jianzhong Qi, Chaochao Chen, Longbiao Chen
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
(WWW-23)
@article{zheng2023increase, title={INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong and Chen, Chaochao and Chen, Longbiao}, journal={arXiv preprint arXiv:2302.02738}, year={2023} }
Zheng Wang, Xiaoliang Fan*, Jianzhong Qi, Haibing Jin, Peizhen Yang, Siqi Shen, Cheng Wang
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability
Proceedings of the Thirty-Seven AAAI Conference on Artificial Intelligence (AAAI-23)
@article{wang2022federated, title={Federated Graph-based Sampling with Arbitrary Client Availability}, author={Wang, Zheng and Fan, Xiaoliang and Qi, Jianzhong and Jin, Haibing and Yang, Peizhen and Shen, Siqi and Wang, Cheng}, journal={arXiv preprint arXiv:2211.13975}, year={2022} }
Hai Wu, Chenglu Wen*, Wei Li, Xin Li, Ruigang Yang, Cheng Wang
Transformation-Equivariant 3D Object Detection for Autonomous Driving
AAAI
@misc{https://doi.org/10.48550/arxiv.2211.11962, doi = {10.48550/ARXIV.2211.11962}, url = {https://arxiv.org/abs/2211.11962}, author = {Wu, Hai and Wen, Chenglu and Li, Wei and Li, Xin and Yang, Ruigang and Wang, Cheng}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Transformation-Equivariant 3D Object Detection for Autonomous Driving}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} }
2022
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, Spotlight, CCF A, top 5%
@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} }
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}, }
Hai Wu, Jinhao Deng, Chenglu Wen*, Xin Li, Cheng Wang, Jonathan Li
CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds
IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2022.3203163
@ARTICLE{9870747, author={Wu, Hai and Deng, Jinhao and Wen, Chenglu and Li, Xin and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={CasA: A Cascade Attention Network for 3-D Object Detection From LiDAR Point Clouds}, year={2022}, volume={60}, number={}, pages={1-11}, doi={10.1109/TGRS.2022.3203163}}
Wenkai Han, Hai Wu, Chenglu Wen*, et al
BLNet: Bidirectional Learning Network for Point Clouds
Computational Visual Media,DOI: 10.1007/s41095-021-0260-6
@article{Han2022, author = {Wenkai Han and Hai Wu and Chenglu Wen and Cheng Wang and Xin Li}, title = {BLNet: Bidirectional learning network for point clouds}, year = {2022}, journal = {Computational Visual Media}, volume = {8}, number = {4}, pages = {585-596}, keywords = {point clouds, irregularity, shape features, bidirectional learning}, url = {https://www.sciopen.com/article/10.1007/s41095-021-0260-6}, doi = {10.1007/s41095-021-0260-6}, }
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} }
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
@misc{https://doi.org/10.48550/arxiv.2203.14698, doi = {10.48550/ARXIV.2203.14698}, url = {https://arxiv.org/abs/2203.14698}, author = {Li, Jialian and Zhang, Jingyi and Wang, Zhiyong and Shen, Siqi and Wen, Chenglu and Ma, Yuexin and Xu, Lan and Yu, Jingyi and Wang, Cheng}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }
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
@misc{https://doi.org/10.48550/arxiv.2203.09215, doi = {10.48550/ARXIV.2203.09215}, url = {https://arxiv.org/abs/2203.09215}, author = {Dai, Yudi and Lin, Yitai and Wen, Chenglu and Shen, Siqi and Xu, Lan and Yu, Jingyi and Ma, Yuexin and Wang, Cheng}, keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }
2021
Linwei Chen, Bowen Fang, Lei Zhao, Yu Zang* , et al.
DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds
International Journal of Applied Earth Observation and Geoinformation
@article{0DeepUrbanDownscale, title={DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds - ScienceDirect}, author={ Lc, A and Bf, C and Lei, Z. C. and Yu, Z. A. and Wl, A and Yc, A and Cheng, W. A. and Jla, B }, journal={International Journal of Applied Earth Observation and Geoinformation}, volume={106}, }
Chuanpan Zheng, Cheng Wang*, Xiaoliang Fan, Jianzhong Qi, Xu Yan
STPC-Net: Learn Massive Geo-sensory Data as Spatio-Temporal Point Clouds
IEEE Transactions on Intelligent Transportation Systems
@article{zheng2021stpc, title={STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds}, author={Zheng, Chuanpan and Wang, Cheng and Fan, Xiaoliang and Qi, Jianzhong and Yan, Xu}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2021}, publisher={IEEE} }
Yongquan Fu, Lun An, Kai Chen, Pere Barlet-Ros, Siqi Shen*
Jellyfish: Locality-sensitive Subflow Sketching
INFOCOM, 2021, CCF A
@article{Fu2021JellyfishLS, title={Jellyfish: Locality-Sensitive Subflow Sketching}, author={Yongquan Fu and Lun An and Siqi Shen and Kai Chen and Pere Barlet-Ros}, journal={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications}, year={2021}, pages={1-10} }
Siqi Shen, Yongquan Fu*, Huayou Su, Hengyue Pan, Peng Qiao, Yong Dou, Cheng Wang*
GRAPHCOMM: A GRAPH NEURAL NETWORK BASED METHOD FOR MULTI-AGENT REINFORCEMENT LEARNING
ICASSP 2021, CCF B
@inproceedings{GraphComm, author = {Siqi Shen and Yongquan Fu and Huayou Su and Hengyue Pan and Peng Qiao and Yong Dou and Cheng Wang}, title = {Graphcomm: {A} Graph Neural Network Based Method for Multi-Agent Reinforcement Learning}, booktitle = {{ICASSP}}, pages = {3510--3514}, year = {2021}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Zheng Wang#, Xiaoliang Fan*, Jianzhong Qi, Cheng Wang, Rongshan Yu, Chenglu Wen
Federated Learning with Fair Averaging
30th International Joint Conference on Artificial Intelligence (IJCAI-21)
@article{wang2021federated, title={Federated Learning with Fair Averaging}, author={Wang, Zheng and Fan, Xiaoliang and Qi, Jianzhong and Wen, Chenglu and Wang, Cheng and Yu, Rongshan}, journal={arXiv preprint arXiv:2104.14937}, year={2021} }
Hai Wu, Qing Li, Chenglu Wen*, Xin Li, Xiaoliang Fan, Cheng Wang
Tracklet Proposal Network for Multi-Object Tracking on Point Clouds
IJCAI
@inproceedings{ijcai2021p161, title = {Tracklet Proposal Network for Multi-Object Tracking on Point Clouds}, author = {Wu, Hai and Li, Qing and Wen, Chenglu and Li, Xin and Fan, Xiaoliang and Wang, Cheng}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, editor = {Zhi-Hua Zhou}, pages = {1165--1171}, year = {2021}, month = {8}, note = {Main Track}, doi = {10.24963/ijcai.2021/161}, url = {https://doi.org/10.24963/ijcai.2021/161}, }
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
@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.} }
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
@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.} }
Yudi Dai, Chenglu Wen*, Hai Wu, Yulan Guo, Longbiao Chen, Cheng Wang
Indoor 3D Human Trajectory Reconstruction Using Surveillance Camera Videos and Point Clouds
IEEE Transactions on Circuits and Systems for Video Technology
@ARTICLE{9433501, author={Dai, Yudi and Wen, Chenglu and Wu, Hai and Guo, Yulan and Chen, Longbiao and Wang, Cheng}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, title={Indoor 3D Human Trajectory Reconstruction Using Surveillance Camera Videos and Point Clouds}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TCSVT.2021.3081591}}
Hai Wu, Wenkai Han, Chenglu Wen*, Xin Li, Cheng Wang
3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association
IEEE Transactions on Intelligent Transportation Systems
@ARTICLE{9352500, author={Wu, Hai and Han, Wenkai and Wen, Chenglu and Li, Xin and Wang, Cheng}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association}, year={2021}, volume={}, number={}, pages={1-10}, doi={10.1109/TITS.2021.3055616}}
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)
@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} }
Yongquan Fu, Lun An, Kai Chen, Pere Barlet-Ros, Siqi Shen
Jellyfish: Locality-sensitive Subflow Sketching
INFOCOM 2021, CCF A
312312 321312
2020
Y. Zhang, K. Huo, Z. Liu, Y. Zang*, C. Wang et al
PGNet: A Part-based Generative Network for 3D Object Reconstruction
Knowledge based System
null
W. Zhang, L. Chen, Z. Xiong, Y. Zang* et al.
Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network
ISPRS Journal of Photogrammetry and Remote Sensing
null
Adele Lu Jia, Yuanxing Rao, Hongru Li, Ran Tian, Siqi Shen*
Revealing Donation Dynamics in Social Live Video Streaming
WWW 2020, CCF A
@inbook{10.1145/3366424.3382682, author = {Lu Jia, Adele and Rao, Yuanxing and Li, Hongru and Tian, Ran and Shen, Siqi}, title = {Revealing Donation Dynamics in Social Live Video Streaming}, year = {2020}, isbn = {9781450370240}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3366424.3382682}, abstract = {Social live video streaming has become a global economic and social phenomenon with the rise of platforms like Facebook-Live, Youtube-Live, and Twitch. The phenomenon of user donation in these communities is rapidly emerging, towards which however we have very limited understandings. In this preliminary work, we reveal the dynamics of user donations based on a publicly available (anonymized) dataset with detailed information on over 2 million users and worth in total over 200 million US dollars. Among other results, we find that (i) both the donations received and the donations made are highly skewed, (ii) user donation is strongly correlated with the atmosphere (the volume and the sentiment of real-time user chats) and in the long run, the loss of broadcasters, and (iii) donors are loyal and very generous to their favorite broadcasters while in the mean time they also support others moderately. Our findings represent a first step towards understanding user donations which will shed lights on the donor retention problem and the design of social live video streaming services. }, booktitle = {Companion Proceedings of the Web Conference 2020}, pages = {30–31}, numpages = {2} }
Siqi Shen*, Yongquan Fu, Adele Lu Jia, Huayou Su, Qinglin Wang, Chengsong Wang, Yong Dou
Learning Network Representation Through Reinforcement Learning
ICASSP 2020, CCF B
@INPROCEEDINGS{9053879, author={Shen, Siqi and Fu, Yongquan and Jia, Adele Lu and Su, Huayou and Wang, Qinglin and Wang, Chengsong and Dou, Yong}, booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Learning Network Representation Through Reinforcement Learning}, year={2020}, volume={}, number={}, pages={3537-3541}, doi={10.1109/ICASSP40776.2020.9053879}}
Yongquan Fu, Dongsheng Li, Siqi Shen*, Yiming Zhang, Kai Chen
Clustering-preserving Network Flow Sketching
INFOCOM 2020, CCF A
@INPROCEEDINGS{9155388, author={Fu, Yongquan and Li, Dongsheng and Shen, Siqi and Zhang, Yiming and Chen, Kai}, booktitle={IEEE INFOCOM 2020 - IEEE Conference on Computer Communications}, title={Clustering-preserving Network Flow Sketching}, year={2020}, volume={}, number={}, pages={1309-1318}, doi={10.1109/INFOCOM41043.2020.9155388}}
Xiaoxue Shen, Adele Lu Jia, Siqi Shen*, Yong Dou, Helping the ineloquent farmers: Finding experts for questions with limited text in agricultural Q&A Communities
Helping the ineloquent farmers: Finding experts for questions with limited text in agricultural Q&A Communities
IEEE ACCESS 2020, JCR 2
@ARTICLE{9050735, author={Shen, Xiaoxue and Jia, Adele Lu and Shen, Siqi and Dou, Yong}, journal={IEEE Access}, title={Helping the Ineloquent Farmers: Finding Experts for Questions With Limited Text in Agricultural Q amp;A Communities}, year={2020}, volume={8}, number={}, pages={62238-62247}, doi={10.1109/ACCESS.2020.2984342}}
Chuanpan Zheng#, Xiaoliang Fan*, Jianzhong Qi, Cheng Wang
GMAN: A Graph Multi-Attention Network for Traffic Prediction
34th AAAI Conference on Artificial Intelligence (AAAI-20)
@inproceedings{zheng2020gman, title={Gman: A graph multi-attention network for traffic prediction}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={34}, number={01}, pages={1234--1241}, year={2020} }
W. Zhang, L. Chen, Z. Xiong, Y. Zang et al.
Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network
ISPRS Journal of Photogrammetry and Remote Sensing
@article{zhang2020large, title={Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network}, author={Zhang, Weini and Chen, Linwei and Xiong, Zhangyue and Zang, Yu and Li, Jonathan and Zhao, Lei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={164}, pages={97--105}, year={2020}, publisher={Elsevier} }
Y. Zhang, K. Huo, Z. Liu, Y. Zang, C. Wang et al.
PGNet: A Part-based Generative Network for 3D Object Reconstruction
Knowledge based System
@article{zhang2020pgnet, title={PGNet: A Part-based Generative Network for 3D object reconstruction}, author={Zhang, Yang and Huo, Kai and Liu, Zhen and Zang, Yu and Liu, Yongxiang and Li, Xiang and Zhang, Qianyu and Wang, Cheng}, journal={Knowledge-Based Systems}, volume={194}, pages={105574}, year={2020}, publisher={Elsevier} }
Cheng Wang, Yudi Dai, Naser Elsheimy, Chenglu Wen, Guenther Retscher, Zhizhong Kang, Andrea Lingua
ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING.
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences
@article{wang2020isprs, title={ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING.}, author={Wang, Cheng and Dai, Yudi and Elsheimy, Naser and Wen, Chenglu and Retscher, Guenther and Kang, Zhizhong and Lingua, Andrea}, journal={ISPRS Annals of Photogrammetry, Remote Sensing \& Spatial Information Sciences}, volume={5}, number={5}, year={2020} }
Wei Li, Cheng Wang, Congren Lin, Guobao Xiao, Chenglu Wen, Jonathan Li
Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization
ISPRS Journal of Photogrammetry and Remote Sensing, DOI:10.1016/j.isprsjprs.2020.01.021, 2020
@article{LI2020284, title = {Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {163}, pages = {284-299}, year = {2020}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2020.01.021}, url = {https://www.sciencedirect.com/science/article/pii/S0924271620300277}, author = {Wei Li and Cheng Wang and Congren Lin and Guobao Xiao and Chenglu Wen and Jonathan Li}, keywords = {Supervoxel segmentation, Non-cooperative game, Keypoint correspondences, Point cloud registration}, abstract = {As a key step in Six-Degree-of-Freedom (6DoF) point cloud registration, 3D keypoint technique aims to extract matches or inliers from random correspondences between the two keypoint sets. The major challenge in 3D keypoint techniques is the high ratio of mismatched or outliers in random correspondences in real-world point cloud registration. In this paper, we present a novel inlier extraction method, which is based on Supervoxel Guidance and Game Theory optimization (SGGT), to extract reliable inliers and apply for point cloud registration. Specifically, to reduce the scale of keypoint correspondences, we first construct powerful groups of keypoint correspondences by introducing supervoxels, which involves 3D spatial homogeneity. Second, to select promising combined groups, we present a novel ‘fit-and-remove’ strategy by incorporating 3D local transformation constraints. Third, to extract purer inliers for point cloud registration, we propose a grouping non-cooperative game algorithm, which considers the relationship between the combined groups. The proposed SGGT, by eliminating the mismatched combined groups globally, avoids the false combined groups that lead to the failed estimation of rigid transformations. Experimental results show that when processing on large keypoint sets, the proposed SGGT is over 100 times more efficient compared to the stat-of-the-art, while keeping the similar accuracy.} }
Cheng Wang, Chenglu Wen, Yudi Dai, Shangshu Yu, Minghao Liu
Urban 3D modeling with mobile laser scanning: a review
Virtual Reality & Intelligent Hardware, DOI:10.1016/j.vrih.2020.05.003, 2020
@article{WANG2020175, title = {Urban 3D modeling with mobile laser scanning: a review}, journal = {Virtual Reality & Intelligent Hardware}, volume = {2}, number = {3}, pages = {175-212}, year = {2020}, note = {3D Visual Processing and Reconstruction Special Issue}, issn = {2096-5796}, doi = {https://doi.org/10.1016/j.vrih.2020.05.003}, url = {https://www.sciencedirect.com/science/article/pii/S2096579620300395}, author = {Cheng Wang and Chenglu Wen and Yudi Dai and Shangshu Yu and Minghao Liu}, keywords = {3D Modeling, MMS, LIDAR, Urban}, abstract = {Mobile laser scanning (MLS) systems mainly comprise laser scanners and mobile mapping platforms. Typical MLS systems are able to acquire three-dimensional point clouds with 1-10 centimeter point spacing at a normal driving or walking speed in the street or indoor environments. The MLS' advantages of efficiency and stability make it a quite practical tool for three-dimensional urban modeling. This paper reviews the latest advances in 3D modeling of the LiDAR-based mobile mapping system (MMS) point cloud, including LiDAR Simultaneous Localization and Mapping (SLAM), point cloud registration, feature extraction, object extraction, semantic segmentation, and deep learning processing. Then typical urban modeling applications based on MMS are also discussed.} }
Yi-Ting Cheng, Ankit Patel, Chenglu Wen, Darcy Bullock, Ayman Habib
Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds
Remote Sensing, DOI:10.3390/rs12091379, 2020
@Article{rs12091379, AUTHOR = {Cheng, Yi-Ting and Patel, Ankit and Wen, Chenglu and Bullock, Darcy and Habib, Ayman}, TITLE = {Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds}, JOURNAL = {Remote Sensing}, VOLUME = {12}, YEAR = {2020}, NUMBER = {9}, ARTICLE-NUMBER = {1379}, URL = {https://www.mdpi.com/2072-4292/12/9/1379}, ISSN = {2072-4292}, ABSTRACT = {Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.}, DOI = {10.3390/rs12091379} }
Wenkai Han, Chenglu Wen*, Cheng Wang, Xin Li, Qing Li
Point2Node: Correlation learning of dynamic-node for point cloud feature modeling
AAAI
@article{Han_Wen_Wang_Li_Li_2020, title={Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6725}, DOI={10.1609/aaai.v34i07.6725}, abstractNote={<p>Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node’s correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.</p>}, number={07}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Han, Wenkai and Wen, Chenglu and Wang, Cheng and Li, Xin and Li, Qing}, year={2020}, month={Apr.}, pages={10925-10932} }
S Wang, G Cai*, M Cheng, JM Junior, S Huang, Z Wang, S Su, J Li
Robust 3D reconstruction of building surfaces from point clouds based on structural and closed constraints
ISPRS Journal of Photogrammetry and Remote Sensing
@article{wang2020robust, title={Robust 3D reconstruction of building surfaces from point clouds based on structural and closed constraints}, author={Wang, Senyuan and Cai, Guorong and Cheng, Ming and Junior, Jos{\'e} Marcato and Huang, Shangfeng and Wang, Zongyue and Su, Songzhi and Li, Jonathan}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={170}, pages={29--44}, year={2020}, publisher={Elsevier} }
Yongquan Fu, Dongsheng Li, Siqi Shen, Yiming Zhang, Kai Chen
Clustering-preserving Network Flow Sketching
2020, INFOCOM, CCF A
312312 321312
Siqi Shen, Yongquan Fu, Adele Lu Jia, Huayou Su, Qinglin Wang, Chengsong Wang, Yong Dou
Learning Network Representation Through Reinforcement Learning
ICASSP, 2020, CCF B
312312 321312
Adele Lu Jia, Yuanxing Rao, Hongru Li, Ran Tian, Siqi Shen
Revealing Donation Dynamics in Social Live Video Streaming
WWW 2020, CCF A
312312 321312
Shanxin Zhang, Cheng Wang, Zijian He, Qing Li, Xiuhong Lin, Xin Li, Juyong Zhang,Chenhui Yang, Jonathan Li
Vehicle global 6-DoF pose estimation under traffic surveillance camera
ISPRS Journal of Photogrammetry and Remote Sensing
312312 321312
Zhipeng Luo, Di Liu, Jonathan Li, Yiping Chen, Zhenlong Xiao, José Marcato Junior,Wesley Nunes Gonçalves, Cheng Wang
Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds
ISPRS Journal of Photogrammetry and Remote Sensing
312312 321312
Zheng Gonga,, Haojia Lin, Dedong Zhang, Zhipeng Luo, John Zelek, Yiping Chen,Abdul Nurunnabi, Cheng Wang, Jonathan Li
A Frustum-based probabilistic framework for 3D object detection by fusion of LiDAR and camera data
ISPRS Journal of Photogrammetry and Remote Sensing
312312 321312
Yangbin Lin, Jialian Li, Cheng Wang, Zhonggui Chen, Zongyue Wang, Jonathan Li
Fast regularity-constrained plane fitting
ISPRS Journal of Photogrammetry and Remote Sensing
312312 321312
2019
Y. Zhang, Z. Xiong, Y. Zang*, et al.
Topology-aware road network extraction via multi-supervised generative adversarial networks
Remote Sensing
null
Liwen Peng, Siqi Shen*, Jun Xu, Yongquan Fu, Dongsheng Li, Adele lu Jia. Diting: An Author Disambiguation method based on Network Representation Learning
Diting: An Author Disambiguation method based on Network Representation Learning
IEEE ACCESS 2019, JCR 2
@ARTICLE{8844683, author={Peng, Liwen and Shen, Siqi and Xu, Jun and Fu, Yongquan and Li, Dongsheng and Jia, Adele Lu}, journal={IEEE Access}, title={Diting: An Author Disambiguation Method Based on Network Representation Learning}, year={2019}, volume={7}, number={}, pages={135539-135555}, doi={10.1109/ACCESS.2019.2942477}}
Yongquan Fu, Dongsheng Li, Pere Barlet-Ros, Chun Huang, Zhen Huang, Siqi Shen, Huayou Su
A Skewness-aware Matrix Factorization Approach for Mesh-structured Cloud Services
IEEE/ACM Transactions on Networking 2019, CCF A
@article{10.1109/TNET.2019.2923815, author = {Fu, Yongquan and Li, Dongsheng and Barlet-Ros, Pere and Huang, Chun and Huang, Zhen and Shen, Siqi and Su, Huayou}, title = {A Skewness-Aware Matrix Factorization Approach for Mesh-Structured Cloud Services}, year = {2019}, issue_date = {August 2019}, publisher = {IEEE Press}, volume = {27}, number = {4}, issn = {1063-6692}, url = {https://doi.org/10.1109/TNET.2019.2923815}, doi = {10.1109/TNET.2019.2923815}, abstract = {Online cloud services need to fulfill clients’ requests scalably and fast. State-of-the-art cloud services are increasingly deployed as a distributed service mesh. Service to service communication is frequent in the mesh. Unfortunately, problematic events may occur between any pair of nodes in the mesh, therefore, it is vital to maximize the network visibility. A state-of-the-art approach is to model pairwise RTTs based on a latent factor model represented as a low-rank matrix factorization. A latent factor corresponds to a rank-1 component in the factorization model, and is shared by all node pairs. However, different node pairs usually experience a skewed set of hidden factors, which should be fully considered in the model. In this paper, we propose a skewness-aware matrix factorization method named SMF. We decompose the matrix factorization into basic units of rank-one latent factors, and progressively combine rank-one factors for different node pairs. We present a unifying framework to automatically and adaptively select the rank-one factors for each node pair, which not only preserves the low rankness of the matrix model, but also adapts to skewed network latency distributions. Over real-world RTT data sets, SMF significantly improves the relative error by a factor of 0.2 $times$ to 10 $times$ , converges fast and stably, and compactly captures fine-grained local and global network latency structures.}, journal = {IEEE/ACM Trans. Netw.}, month = {aug}, pages = {1598–1611}, numpages = {14} }
Liwen Peng, Siqi Shen*, Dongsheng Li, Jun Xu, Yongquan Fu, Huayou Su
Author Disambiguation through Adversarial Network Representation Learning
IJCNN 2019, CCF C
Chuanpan Zheng#, Xiaoliang Fan*, Chenglu Wen, Cheng Wang
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems (TITS)
@article{zheng2019deepstd, title={Deepstd: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wen, Chenglu and Chen, Longbiao and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={21}, number={9}, pages={3744--3755}, year={2019}, publisher={IEEE} }
Y. Zhang, Z. Xiong, Y. Zang, et al.
Topology-aware road network extraction via multi-supervised generative adversarial networks
Remote Sensing
@article{zhang2019topology, title={Topology-aware road network extraction via multi-supervised generative adversarial networks}, author={Zhang, Yang and Xiong, Zhangyue and Zang, Yu and Wang, Cheng and Li, Jonathan and Li, Xiang}, journal={Remote Sensing}, volume={11}, number={9}, pages={1017}, year={2019}, publisher={Multidisciplinary Digital Publishing Institute} }
Zongliang Zhang, Chenglu Wen, Yiping Chen, Wei Li, Changbin You, Chao Wang, J Li
Indoor scene registration based on siamese network and pointnet
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
@article{zhang2019indoor, title={Indoor scene registration based on siamese network and pointnet}, author={Zhang, Zongliang and Wen, Chenglu and Chen, Yiping and Li, Wei and You, Changbin and Wang, Chao and Li, J}, journal={ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume={4}, pages={307--312}, year={2019}, publisher={Copernicus GmbH} }
Cheng Wang, Yudi Dai, Naser El-Sheimy, Chenglu Wen, Guenther Retscher, Zhizhong Kang, Andrea Lingua
PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING.
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences
@article{wang2019progress, title={PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING.}, author={Wang, Cheng and Dai, Yudi and El-Sheimy, Naser and Wen, Chenglu and Retscher, Guenther and Kang, Zhizhong and Lingua, Andrea}, journal={International Archives of the Photogrammetry, Remote Sensing \& Spatial Information Sciences}, year={2019} }
Chenglu Wen, Changbin You, Hai Wu, Cheng Wang, Xiaoliang Fan, Jonathan Li
Recovery of urban 3D road boundary via multi-source data
ISPRS Journal of Photogrammetry and Remote Sensing
@article{WEN2019184, title = {Recovery of urban 3D road boundary via multi-source data}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {156}, pages = {184-201}, year = {2019}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2019.08.010}, url = {https://www.sciencedirect.com/science/article/pii/S0924271619301947}, author = {Chenglu Wen and Changbin You and Hai Wu and Cheng Wang and Xiaoliang Fan and Jonathan Li}, keywords = {Point clouds, Mobile laser scanning, 3D road boundary, Multi-source data, GPS trajectory data, Remote sensing image}, abstract = {The mapping of road boundaries provides critical information about roads for urban road traffic safety. This paper presents a deep learning-based framework for recovering 3D road boundary using multi-source data, which include mobile laser scanning (MLS) point clouds, spatial trajectory data, and remote sensing images. The proposed road recovery method uses extracted 3D road boundaries from MLS point clouds as inputs. First, after automatic erroneous boundary removal, a CNN-based boundary completion model completes road boundaries. Then, to refine the imperfect road boundaries, road centerlines generated from dynamic taxi GPS trajectory data and remote sensing images are used as completion guidance for a generative adversarial nets model to obtain more accurate and complete road boundaries. Finally, after associating a sequence of taxi GPS recorded trajectory points with the correct 3D road boundaries, inherent geometric road characteristics and road dynamic information are extracted from the complete boundaries and taxi GPS trajectory data, respectively. The testing dataset contains two urban road MLS datasets, and the KITTI dataset. The experimental results on point clouds from different sensors demonstrate our proposed method is effective and promising for recovering 3D road boundary and extracting road characteristics.} }
Shanxin Zhang, Cheng Wang, Lili Lin, Chenglu Wen, Chenhui Yang, Zhemin Zhang, Jonathan Li
Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds
Remote Sensing, DOI:10.3390/rs11121453, 2019
@Article{rs11121453, AUTHOR = {Zhang, Shanxin and Wang, Cheng and Lin, Lili and Wen, Chenglu and Yang, Chenhui and Zhang, Zhemin and Li, Jonathan}, TITLE = {Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds}, JOURNAL = {Remote Sensing}, VOLUME = {11}, YEAR = {2019}, NUMBER = {12}, ARTICLE-NUMBER = {1453}, URL = {https://www.mdpi.com/2072-4292/11/12/1453}, ISSN = {2072-4292}, ABSTRACT = {Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.}, DOI = {10.3390/rs11121453} }
Chenglu Wen*, Yudi Dai, Yan Xia, Yuhan Lian, Jinbin Tan, Cheng Wang, Jonathan Li
Toward Efficient 3-D Colored Mapping in GPS-/GNSS-Denied Environments
IEEE Geoscience and Remote Sensing Letters, DOI:10.1109/LGRS.2019.2916844, 2019
@ARTICLE{8736839, author={Wen, Chenglu and Dai, Yudi and Xia, Yan and Lian, Yuhan and Tan, Jinbin and Wang, Cheng and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, title={Toward Efficient 3-D Colored Mapping in GPS-/GNSS-Denied Environments}, year={2020}, volume={17}, number={1}, pages={147-151}, doi={10.1109/LGRS.2019.2916844}}
Zheng Gong, Jonathan Li, Zhipeng Luo, Chenglu Wen, Cheng Wang, John Zelek
Mapping and semantic modeling of underground parking lots using a backpack LiDAR system
IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2019.2955734, 2019
@ARTICLE{8924918, author={Gong, Zheng and Li, Jonathan and Luo, Zhipeng and Wen, Chenglu and Wang, Cheng and Zelek, John}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Mapping and Semantic Modeling of Underground Parking Lots Using a Backpack LiDAR System}, year={2021}, volume={22}, number={2}, pages={734-746}, doi={10.1109/TITS.2019.2955734}}
Chuanpan Zheng, Xiaoliang Fan, Chenglu Wen, Longbiao Chen, Cheng Wang, Jonathan Li
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2019.2932785, 2019
@ARTICLE{8793226, author={Zheng, Chuanpan and Fan, Xiaoliang and Wen, Chenglu and Chen, Longbiao and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction}, year={2020}, volume={21}, number={9}, pages={3744-3755}, doi={10.1109/TITS.2019.2932785}}
Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, Zijian He
Rf-net: An end-to-end image matching network based on receptive field
CVPR, DOI:10.1109/CVPR.2019.00832, 2019
@InProceedings{Shen_2019_CVPR, author = {Shen, Xuelun and Wang, Cheng and Li, Xin and Yu, Zenglei and Li, Jonathan and Wen, Chenglu and Cheng, Ming and He, Zijian}, title = {RF-Net: An End-To-End Image Matching Network Based on Receptive Field}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2019} }
Chenglu Wen, Xiaotian Sun, Jonathan Li, Cheng Wang, Yan Guo, Ayman Habib
A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds
ISPRS journal of photogrammetry and remote sensing, DOI:10.1016/j.isprsjprs.2018.10.007, 2019
@article{WEN2019178, title = {A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {147}, pages = {178-192}, year = {2019}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2018.10.007}, url = {https://www.sciencedirect.com/science/article/pii/S0924271618302855}, author = {Chenglu Wen and Xiaotian Sun and Jonathan Li and Cheng Wang and Yan Guo and Ayman Habib}, keywords = {Point cloud, Road marking, Extraction, Classification, Completion, Deep learning}, abstract = {Road markings play a critical role in road traffic safety and are one of the most important elements for guiding autonomous vehicles (AVs). High-Definition (HD) maps with accurate road marking information are very useful for many applications ranging from road maintenance, improving navigation, and prediction of upcoming road situations within AVs. This paper presents a deep learning-based framework for road marking extraction, classification and completion from three-dimensional (3D) mobile laser scanning (MLS) point clouds. Compared with existing road marking extraction methods, which are mostly based on intensity thresholds, our method is less sensitive to data quality. We added the step of road marking completion to further optimize the results. At the extraction stage, a modified U-net model was used to segment road marking pixels to overcome the intensity variation, low contrast and other issues. At the classification stage, a hierarchical classification method by integrating multi-scale clustering with Convolutional Neural Networks (CNN) was developed to classify different types of road markings with considerable differences. At the completion stage, a method based on a Generative Adversarial Network (GAN) was developed to complete small-size road markings first, then followed by completing broken lane lines and adding missing markings using a context-based method. In addition, we built a point cloud road marking dataset to train the deep network model and evaluate our method. The dataset contains urban road and highway MLS data and underground parking lot data acquired by our own assembled backpacked laser scanning system. Our experimental results obtained using the point clouds of different scenes demonstrated that our method is very promising for road marking extraction, classification and completion.} }
Qing Li, Shaoyang Chen, Cheng Wang, Xin Li, Chenglu Wen, Ming Cheng, Jonathan Li
Lo-net: Deep real-time lidar odometry
CVPR,DOI:10.1109/CVPR.2019.00867, 2019
@InProceedings{Li_2019_CVPR, author = {Li, Qing and Chen, Shaoyang and Wang, Cheng and Li, Xin and Wen, Chenglu and Cheng, Ming and Li, Jonathan}, title = {LO-Net: Deep Real-Time Lidar Odometry}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2019} }
Q Fan, F Chen*, M Cheng, S Lou, R Xiao, B Zhang, C Wang, J Li
Ship detection using a fully convolutional network with compact polarimetric SAR images
Remote Sensing
@article{fan2019ship, title={Ship detection using a fully convolutional network with compact polarimetric SAR images}, author={Fan, Qiancong and Chen, Feng and Cheng, Ming and Lou, Shenlong and Xiao, Rulin and Zhang, Biao and Wang, Cheng and Li, Jonathan}, journal={Remote Sensing}, volume={11}, number={18}, pages={2171}, year={2019}, publisher={Multidisciplinary Digital Publishing Institute} }
C Wang, M Cheng*, F Sohel, M Bennamoun, J Li
NormalNet: A voxel-based CNN for 3D object classification and retrieval
Neurocomputing
@article{wang2019normalnet, title={NormalNet: A voxel-based CNN for 3D object classification and retrieval}, author={Wang, Cheng and Cheng, Ming and Sohel, Ferdous and Bennamoun, Mohammed and Li, Jonathan}, journal={Neurocomputing}, volume={323}, pages={139--147}, year={2019}, publisher={Elsevier} }
Li, Q., Chen, S., Wang, C., Li, X., Wen, C., Cheng, M., Li, J.
Lo-net: Deep real-time lidar odometry
CVPR 2019
@inproceedings{DBLP:conf/cvpr/LiC0LW0L19, author = {Qing Li and Shaoyang Chen and Cheng Wang and Xin Li and Chenglu Wen and Ming Cheng and Jonathan Li}, title = {LO-Net: Deep Real-Time Lidar Odometry}, booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019}, pages = {8473--8482}, publisher = {Computer Vision Foundation / {IEEE}}, year = {2019}, url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Li\_LO-Net\_Deep\_Real-Time\_Lidar\_Odometry\_CVPR\_2019\_paper.html}, doi = {10.1109/CVPR.2019.00867}, timestamp = {Mon, 30 Aug 2021 17:01:14 +0200}, biburl = {https://dblp.org/rec/conf/cvpr/LiC0LW0L19.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Shen, X., Wang, C., Li, X., Yu, Z., Li, J., Wen, C., Cheng, M., He, Z
RF-net: An end-to-end image matching network based on receptive field
CVPR 2019
@inproceedings{DBLP:conf/cvpr/Shen0LYLW0H19, author = {Xuelun Shen and Cheng Wang and Xin Li and Zenglei Yu and Jonathan Li and Chenglu Wen and Ming Cheng and Zijian He}, title = {RF-Net: An End-To-End Image Matching Network Based on Receptive Field}, booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019}, pages = {8132--8140}, publisher = {Computer Vision Foundation / {IEEE}}, year = {2019}, url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Shen\_RF-Net\_An\_End-To-End\_Image\_Matching\_Network\_Based\_on\_Receptive\_Field\_CVPR\_2019\_paper.html}, doi = {10.1109/CVPR.2019.00832}, timestamp = {Mon, 30 Aug 2021 17:01:14 +0200}, biburl = {https://dblp.org/rec/conf/cvpr/Shen0LYLW0H19.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Liu, W., Wang, C., Zang, Y., Lai, S.-H., Weng, D., Sian, X., Lin, X., Shen, X., Li, J.
Ground camera images and UAV 3D model registration for outdoor augmented reality
IEEE VR 2019
@inproceedings{DBLP:conf/vr/LiuWZLWSLSL19, author = {Weiquan Liu and Cheng Wang and Yu Zang and Shang{-}Hong Lai and Dongdong Weng and Xuesheng Sian and Xiuhong Lin and Xuelun Shen and Jonathan Li}, title = {Ground Camera Images and {UAV} 3D Model Registration for Outdoor Augmented Reality}, booktitle = {{IEEE} Conference on Virtual Reality and 3D User Interfaces, {VR} 2019, Osaka, Japan, March 23-27, 2019}, pages = {1050--1051}, publisher = {{IEEE}}, year = {2019}, url = {https://doi.org/10.1109/VR.2019.8797821}, doi = {10.1109/VR.2019.8797821}, timestamp = {Fri, 09 Apr 2021 18:51:40 +0200}, biburl = {https://dblp.org/rec/conf/vr/LiuWZLWSLSL19.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Yongquan Fu, Dongsheng Li, Pere Barlet-Ros, Chun Huang, Zhen Huang, Siqi Shen, Huayou Su
A Skewness-aware Matrix Factorization Approach for Mesh-structured Cloud Services
IEEE/ACM Transactions on Networking, 2019, CCF A
312312 321312
Yongquan Fu, Dongsheng Li, Siqi Shen, Yiming Zhang, Kai Chen
Resilient Disaggregated Network Flow Monitoring
SIGCOMM, CCF A
312312 321312
2018
Jun Xu, Siqi Shen*, Dongsheng Li, Yongquan Fu
A Network-embedding Based Method for Author Disambiguation
CIKM 2018, CCF B
Adele Lu Jia, Siqi Shen*, Dongsheng Li, and Shengling Chen
Predicting the Implicit and the Explicit Video Popularity in a User Generated Content Site with Enhanced Social Features
Computer Networks 2018, CCF B
L. Luo, Y. Zang*, X. Wang, et al
Estimating Road Widths From Remote Sensing Images
Remote Sensing Letters
@article{luo2018estimating, title={Estimating Road Widths From Remote Sensing Images}, author={Luo, Lun and Zang, Yu and Wang, Xiaofang and Wang, Cheng and Li, Jonathan and Wu, Sheng and Liu, Yuelei}, journal={Remote Sensing Letters}, volume={9}, number={9}, pages={819--828}, year={2018}, publisher={Taylor \& Francis} }
Chenglu Wen, Yan Xia, Yuhan Lian, Yudi Dai, Jinbin Tan, Cheng Wang, Jonathan Li
MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
@article{wen2018mobile, title={MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING}, author={Wen, Chenglu and Xia, Yan and Lian, Yuhan and Dai, Yudi and Tan, Jinbin and Wang, Cheng and Li, Jonathan}, journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume={42}, pages={1}, year={2018} }
Changbin You, Chenglu Wen*, Cheng Wang, Jonathan Li, Ayman Habib
Joint 2-D–3-D traffic sign landmark data set for geo-localization using mobile laser scanning data
IEEE Transactions on Intelligent Transportation Systems
@ARTICLE{8478211, author={You, Changbin and Wen, Chenglu and Wang, Cheng and Li, Jonathan and Habib, Ayman}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Joint 2-D–3-D Traffic Sign Landmark Data Set for Geo-Localization Using Mobile Laser Scanning Data}, year={2019}, volume={20}, number={7}, pages={2550-2565}, doi={10.1109/TITS.2018.2868168}}
Huan Luo, Cheng Wang, Chenglu Wen, Ziyi Chen, Dawei Zai, Yongtao Yu, Jonathan Li
Semantic labeling of mobile LiDAR point clouds via active learning and higher order MRF
IEEE Transactions on Geoscience and Remote Sensing, DOI:10.1109/TGRS.2018.2802935, 2018
@ARTICLE{8353504, author={Luo, Huan and Wang, Cheng and Wen, Chenglu and Chen, Ziyi and Zai, Dawei and Yu, Yongtao and Li, Jonathan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF}, year={2018}, volume={56}, number={7}, pages={3631-3644}, doi={10.1109/TGRS.2018.2802935}}
Cheng Wang, Shiwei Hou, Chenglu Wen*, Zheng Gong, Qing Li, Xiaotian Sun, Jonathan Li
Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud
ISPRS journal of photogrammetry and remote sensing
@article{WANG2018150, title = {Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {143}, pages = {150-166}, year = {2018}, note = {ISPRS Journal of Photogrammetry and Remote Sensing Theme Issue “Point Cloud Processing”}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2018.03.025}, url = {https://www.sciencedirect.com/science/article/pii/S092427161830090X}, author = {Cheng Wang and Shiwei Hou and Chenglu Wen and Zheng Gong and Qing Li and Xiaotian Sun and Jonathan Li}, keywords = {Point clouds, Indoor modeling, Mobile laser scanning, Line framework extraction, Semantic labeling}, abstract = {Indoor building models are essential in many indoor applications. These models are composed of the primitives of the buildings, such as the ceilings, floors, walls, windows, and doors, but not the movable objects in the indoor spaces, such as furniture. This paper presents, for indoor environments, a novel semantic line framework-based modeling building method using backpacked laser scanning point cloud data. The proposed method first semantically labels the raw point clouds into the walls, ceiling, floor, and other objects. Then line structures are extracted from the labeled points to achieve an initial description of the building line framework. To optimize the detected line structures caused by furniture occlusion, a conditional Generative Adversarial Nets (cGAN) deep learning model is constructed. The line framework optimization model includes structure completion, extrusion removal, and regularization. The result of optimization is also derived from a quality evaluation of the point cloud. Thus, the data collection and building model representation become a united task-driven loop. The proposed method eventually outputs a semantic line framework model and provides a layout for the interior of the building. Experiments show that the proposed method effectively extracts the line framework from different indoor scenes.} }
Q Fan, F Chen*, M Cheng, C Wang, J Li
A modified framework for ship detection from compact polarization SAR image
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium
@inproceedings{fan2018modified, title={A modified framework for ship detection from compact polarization SAR image}, author={Fan, Qiancong and Chen, Feng and Cheng, Ming and Wang, Cheng and Li, Jonathan}, booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium}, pages={3539--3542}, year={2018}, organization={IEEE} }
C Chen, X Fan*, C Zheng, L Xiao, M Cheng, C Wang
Sdcae: Stack denoising convolutional autoencoder model for accident risk prediction via traffic big data
2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)
@inproceedings{chen2018sdcae, title={Sdcae: Stack denoising convolutional autoencoder model for accident risk prediction via traffic big data}, author={Chen, Chao and Fan, Xiaoliang and Zheng, Chuanpan and Xiao, Lujing and Cheng, Ming and Wang, Cheng}, booktitle={2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)}, pages={328--333}, year={2018}, organization={IEEE} }
Adele Lu Jia, Siqi Shen*, Dongsheng Li, and Shengling Chen
Predicting the Implicit and the Explicit Video Popularity in a User Generated Content Site with Enhanced Social Features, Computer Networks
2018, CCF B
312312 321312
2017
Ding, L. and Huang, H. and Zang, Y*
Image Quality Assessment Using Directional Anisotropy Structure Measurement
IEEE Transactions on Image Processing
@article{2017Image, title={Image Quality Assessment Using Directional Anisotropy Structure Measurement}, author={ Ding, L. and Huang, H. and Zang, Y. }, journal={IEEE Transactions on Image Processing}, volume={26}, number={4}, pages={1799-1809}, year={2017}, }
Y. Zang*, C. Wang, Y. Yu, L. Luo, Y. Ke, J. Li.
Joint Enhancing Filtering for Road Network Extraction
IEEE Transactions on Geoscience and Remote Sensing
null
Dongsheng Li, Wangxing Zhang, Siqi Shen, Yiming Zhang
SES-LSH: Shuffle-Efficient Locality Sensitive Hashing for Distributed Similarity Search
ICWS 2017, CCF B
Y. Zang, C. Wang, Y. Yu, L. Luo, Y. Ke, J. Li
Joint Enhancing Filtering for Road Network Extraction
IEEE Transactions on Geoscience and Remote Sensing
@article{zang2016joint, title={Joint enhancing filtering for road network extraction}, author={Zang, Yu and Wang, Cheng and Yu, Yao and Luo, Lun and Yang, Ke and Li, Jonathan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, volume={55}, number={3}, pages={1511--1525}, year={2016}, publisher={IEEE} }
D. Li, H. Huang, Y. Zang
Image Quality Assessment Using Directional Anisotropy Structure Measurement
IEEE Transactions on Image Processing
@ARTICLE{7847359, author={Ding, Li and Huang, Hua and Zang, Yu}, journal={IEEE Transactions on Image Processing}, title={Image Quality Assessment Using Directional Anisotropy Structure Measurement}, year={2017}, volume={26}, number={4}, pages={1799-1809}, doi={10.1109/TIP.2017.2665972}}
Zheng Gong, Chenglu Wen*, Cheng Wang, Jonathan Li
A target-free automatic self-calibration approach for multibeam laser scanners
IEEE Transactions on Instrumentation and Measurement
@ARTICLE{8067639, author={Gong, Zheng and Wen, Chenglu and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Instrumentation and Measurement}, title={A Target-Free Automatic Self-Calibration Approach for Multibeam Laser Scanners}, year={2018}, volume={67}, number={1}, pages={238-240}, doi={10.1109/TIM.2017.2757148}}
P Huang, M Cheng*, Y Chen, H Luo, C Wang, J Li*
Traffic sign occlusion detection using mobile laser scanning point clouds
IEEE Transactions on Intelligent Transportation Systems
@article{huang2017traffic, title={Traffic sign occlusion detection using mobile laser scanning point clouds}, author={Huang, Pengdi and Cheng, Ming and Chen, Yiping and Luo, Huan and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={18}, number={9}, pages={2364--2376}, year={2017}, publisher={IEEE} }
P Huang, M Cheng*, Y Chen, D Zai, C Wang, J Li*
Solar potential analysis method using terrestrial laser scanning point clouds
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
@article{huang2017solar, title={Solar potential analysis method using terrestrial laser scanning point clouds}, author={Huang, Pengdi and Cheng, Ming and Chen, Yiping and Zai, Dawei and Wang, Cheng and Li, Jonathan}, journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume={10}, number={3}, pages={1221--1233}, year={2017}, publisher={IEEE} }
D Zai, J Li*, Y Guo, M Cheng, Y Lin, H Luo, C Wang
3-D road boundary extraction from mobile laser scanning data via supervoxels and graph cuts
IEEE Transactions on Intelligent Transportation Systems
@article{zai20173, title={3-D road boundary extraction from mobile laser scanning data via supervoxels and graph cuts}, author={Zai, Dawei and Li, Jonathan and Guo, Yulan and Cheng, Ming and Lin, Yangbin and Luo, Huan and Wang, Cheng}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={19}, number={3}, pages={802--813}, year={2017}, publisher={IEEE} }
X Zou, M Cheng*, C Wang, Y Xia, J Li
Tree classification in complex forest point clouds based on deep learning
IEEE Geoscience and Remote Sensing Letters
@article{zou2017tree, title={Tree classification in complex forest point clouds based on deep learning}, author={Zou, Xinhuai and Cheng, Ming and Wang, Cheng and Xia, Yan and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, volume={14}, number={12}, pages={2360--2364}, year={2017}, publisher={IEEE} }
D Zai, J Li*, Y Guo, M Cheng, P Huang, X Cao, C Wang
Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game
ISPRS Journal of Photogrammetry and Remote Sensing
@article{zai2017pairwise, title={Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game}, author={Zai, Dawei and Li, Jonathan and Guo, Yulan and Cheng, Ming and Huang, Pengdi and Cao, Xiaofei and Wang, Cheng}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={134}, pages={15--29}, year={2017}, publisher={Elsevier} }
Dongsheng Li, Wangxing Zhang, Siqi Shen, Yiming Zhang, SES-LSH
Shuffle-Efficient Locality Sensitive Hashing for Distributed Similarity Search
IEEE International Conference on Web Services (ICWS), 2017, CCF B
312312 321312
2016
Y. Zang, C. Wang*, L. Cao, Y. Yu, J. Li.
Road Network Extraction via Aperiodic Directional Structure Measurement
IEEE Transactions on Geoscience and Remote Sensing
null
Adele Lu Jia, Siqi Shen*, Dick Epema, and Alexandru Iosup
When game becomes life: The creators and the spectators of online game replays and live streaming
TOMM 2016, CCF B
F Huang, C Wen*, H Luo, M Cheng, C Wang, J Li
Local quality assessment of point clouds for indoor mobile mapping
Neurocomputing
Chenglu Wen*, Siyu Pan, Cheng Wang, Jonathan Li
An indoor backpack system for 2-D and 3-D mapping of building interiors
IEEE Geoscience and Remote Sensing Letters
@article{wen2016indoor, title={An indoor backpack system for 2-D and 3-D mapping of building interiors}, author={Wen, Chenglu and Pan, Siyu and Wang, Cheng and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, volume={13}, number={7}, pages={992--996}, year={2016}, publisher={IEEE} }
Y. Zang, C. Wang, L. Cao, Y. Yu, J. Li
Road Network Extraction via Aperiodic Directional Structure Measurement
IEEE Transactions on Geoscience and Remote Sensing
@article{zang2016road, title={Road network extraction via aperiodic directional structure measurement}, author={Zang, Yu and Wang, Cheng and Cao, Liujuan and Yu, Yao and Li, Jonathan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, volume={54}, number={6}, pages={3322--3335}, year={2016}, publisher={IEEE} }
X Guo, Y Chen, C Wang, M Cheng, C Wen, J Yu
Automatic shape-based target extraction for close-range photogrammetry
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
@inproceedings{guo2016automatic, title={Automatic shape-based target extraction for close-range photogrammetry}, author={Guo, X and Chen, Y and Wang, C and Cheng, M and Wen, C and Yu, J}, booktitle={Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.}, volume={41}, pages={583--587}, year={2016} }
Fangfang Huang, Chenglu Wen*, Huan Luo, Ming Cheng, Cheng Wang, Jonathan Li
Local quality assessment of point clouds for indoor mobile mapping
Neurocomputing, DOI:10.1016/j.neucom.2016.02.033
@article{HUANG201659, title = {Local quality assessment of point clouds for indoor mobile mapping}, journal = {Neurocomputing}, volume = {196}, pages = {59-69}, year = {2016}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2016.02.033}, url = {https://www.sciencedirect.com/science/article/pii/S0925231216002678}, author = {Fangfang Huang and Chenglu Wen and Huan Luo and Ming Cheng and Cheng Wang and Jonathan Li}, keywords = {Local quality assessment, Indoor mobile mapping, Point clouds, Degradation, Machine learning}, abstract = {The quality of point clouds obtained by RGB-D camera-based indoor mobile mapping can be limited by local degradation because of complex scenarios such as sensor characteristics, partial occlusions, cluttered backgrounds, and complex illumination conditions. This paper presents a machine learning framework to assess the local quality of indoor mobile mapping point cloud data. In our proposed framework, a point cloud dataset with multiple kinds of quality problems is first created by manual annotation and degradation simulation. Then, feature extraction methods based on 3D patches are treated as operating units to conduct quality assessment in local regions. Also, a feature selection algorithm is deployed to obtain the essential components of feature sets that are used to effectively represent local degradation. Finally, a semi-supervised method is introduced to classify quality types of point clouds. Comparative experiments demonstrate that the proposed framework obtained promising quality assessment results with limited labeled data and a large amount of unlabeled data.} }
Yongtao Yu, Jonathan Li, Haiyan Guan, Cheng Wang, Chenglu Wen
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data
IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2016.2550798, 2016
@ARTICLE{7463045, author={Yu, Yongtao and Li, Jonathan and Guan, Haiyan and Wang, Cheng and Wen, Chenglu}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data}, year={2016}, volume={17}, number={12}, pages={3391-3406}, doi={10.1109/TITS.2016.2550798}}
Chenglu Wen*, Siyu Pan, Cheng Wang, Jonathan Li
An indoor backpack system for 2-D and 3-D mapping of building interiors
IEEE Geoscience and Remote Sensing Letters, DOI:10.1109/LGRS.2016.2558486, 2016
@ARTICLE{7468519, author={Wen, Chenglu and Pan, Siyu and Wang, Cheng and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, title={An Indoor Backpack System for 2-D and 3-D Mapping of Building Interiors}, year={2016}, volume={13}, number={7}, pages={992-996}, doi={10.1109/LGRS.2016.2558486}}
Ziyi Chen, Cheng Wang, Huan Luo, Hanyun Wang, Yiping Chen, Chenglu Wen, Yongtao Yu, Liujuan Cao, Jonathan Li
Vehicle detection in high-resolution aerial images based on fast sparse representation classification and multiorder feature
IEEE transactions on intelligent transportation systems, DOI:10.1109/TGRS.2015.2451002, 2016
@ARTICLE{7410075, author={Chen, Ziyi and Wang, Cheng and Luo, Huan and Wang, Hanyun and Chen, Yiping and Wen, Chenglu and Yu, Yongtao and Cao, Liujuan and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature}, year={2016}, volume={17}, number={8}, pages={2296-2309}, doi={10.1109/TITS.2016.2517826}}
Fan Wu, Chenglu Wen, Yulan Guo, Jingjing Wang, Yongtao Yu, Cheng Wang, Jonathan Li
Rapid localization and extraction of street light poles in mobile LiDAR point clouds: A supervoxel-based approach
IEEE Transactions on Intelligent Transportation Systems
@ARTICLE{7497465, author={Wu, Fan and Wen, Chenglu and Guo, Yulan and Wang, Jingjing and Yu, Yongtao and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Rapid Localization and Extraction of Street Light Poles in Mobile LiDAR Point Clouds: A Supervoxel-Based Approach}, year={2017}, volume={18}, number={2}, pages={292-305}, doi={10.1109/TITS.2016.2565698}}
Yongtao Yu, Jonathan Li, Chenglu Wen, Haiyan Guan, Huan Luo, Cheng Wang
Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data
ISPRS journal of photogrammetry and remote sensing
@article{YU2016106, title = {Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {113}, pages = {106-123}, year = {2016}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2016.01.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271616000198}, author = {Yongtao Yu and Jonathan Li and Chenglu Wen and Haiyan Guan and Huan Luo and Cheng Wang}, keywords = {Bag-of-visual-phrases, Deep Boltzmann machine (DBM), Mobile laser scanning (MLS), Point cloud, Traffic sign detection, Traffic sign recognition (TSR)}, abstract = {This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.} }
Haocheng Zhang, Jonathan Li*, Ming Cheng, Cheng Wang
Rapid Inspection of Pavement Markings Using Mobile LIDAR Point Clouds
ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
@article{zhang2016rapid, title={RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS.}, author={Zhang, Haocheng and Li, Jonathan and Cheng, Ming and Wang, Cheng}, journal={International Archives of the Photogrammetry, Remote Sensing \& Spatial Information Sciences}, volume={41}, year={2016} }
F Huang, C Wen*, H Luo, M Cheng, C Wang, J Li
Local quality assessment of point clouds for indoor mobile mapping
Neurocomputing
@article{huang2016local, title={Local quality assessment of point clouds for indoor mobile mapping}, author={Huang, Fangfang and Wen, Chenglu and Luo, Huan and Cheng, Ming and Wang, Cheng and Li, Jonathan}, journal={Neurocomputing}, volume={196}, pages={59--69}, year={2016}, publisher={Elsevier} }
Ziyi Chen, Cheng Wang, Chenglu Wen, Xiuhua Teng, Yiping Chen, Haiyan Guan, Huan Luo, Liujuan Cao, Jonathan Li
Vehicle detection in high-resolution aerial images via sparse representation and superpixels
IEEE Transactions on Geoscience and Remote Sensing
@ARTICLE{7163593, author={Chen, Ziyi and Wang, Cheng and Wen, Chenglu and Teng, Xiuhua and Chen, Yiping and Guan, Haiyan and Luo, Huan and Cao, Liujuan and Li, Jonathan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels}, year={2016}, volume={54}, number={1}, pages={103-116}, doi={10.1109/TGRS.2015.2451002}}
M Cheng, H Zhang, C Wang, J Li*
Extraction and classification of road markings using mobile laser scanning point clouds
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
@article{cheng2016extraction, title={Extraction and classification of road markings using mobile laser scanning point clouds}, author={Cheng, Ming and Zhang, Haocheng and Wang, Cheng and Li, Jonathan}, journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume={10}, number={3}, pages={1182--1196}, year={2016}, publisher={IEEE} }
L Cao, Q Jiang, M Cheng*, C Wang
Robust vehicle detection by combining deep features with exemplar classification
Neurocomputing
@article{cao2016robust, title={Robust vehicle detection by combining deep features with exemplar classification}, author={Cao, Liujuan and Jiang, Qilin and Cheng, Ming and Wang, Cheng}, journal={Neurocomputing}, volume={215}, pages={225--231}, year={2016}, publisher={Elsevier} }
2015
Y. Zang, H. Huang*, L. Zhang.
Guided Adaptive Image Smoothing via Directional Anisotropic Structure Measurement
IEEE Transactions on Visualization and Computer Graphics
null
Adele Lu Jia, Siqi Shen*, Ruud van de Bovenkamp, Alexandru Iosup, Fernando Kuipers, and Dick Epema
Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games
TKDD 2015, CCF B
Siqi Shen, Shunyun Hu, Alexandru Iosup, and Dick Epema
The Area of Simulation Mechanism and Architecture for Multi-Avatar Virtual Environments
TOMM 2015, CCF B
Marcus Martens, Siqi Shen, Alexandru Iosup, and Fernando Kuipers
Toxicity Detection in Multiplayer Online Games
NetGames 2015, BEST Paper
Siqi Shen, Alexandru Iosup, Assaf Israel, Walfredo Cirne, Danny Raz, and Dick Epema
An Availability-on-Demand Mechanism for Datacenters
CCGrid 2015, CCF C
Siqi Shen, Vincent van Beek, and Alexandru Iosup
Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters
CCGrid 2015, CCF C
Hanyun Wang, Huan Luo, Chenglu Wen*, Jun Cheng, Peng Li, Yiping Chen, Cheng Wang, Jonathan Li
Road boundaries detection based on local normal saliency from mobile laser scanning data
IEEE Geoscience and remote sensing letters
@ARTICLE{7153515, author={Wang, Hanyun and Luo, Huan and Wen, Chenglu and Cheng, Jun and Li, Peng and Chen, Yiping and Wang, Cheng and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, title={Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data}, year={2015}, volume={12}, number={10}, pages={2085-2089}, doi={10.1109/LGRS.2015.2449074}}
Zhipeng Cai, Cheng Wang, Chenglu Wen, Jonathan Li
Occluded Boundary Detection for Small-Footprint Groundborne LIDAR Point Cloud Guided by Last Echo
IEEE Geoscience and Remote Sensing Letters
@ARTICLE{7226818, author={Cai, Zhipeng and Wang, Cheng and Wen, Chenglu and Li, Jonathan}, journal={IEEE Geoscience and Remote Sensing Letters}, title={Occluded Boundary Detection for Small-Footprint Groundborne LIDAR Point Cloud Guided by Last Echo}, year={2015}, volume={12}, number={11}, pages={2272-2276}, doi={10.1109/LGRS.2015.2466811}}
Siqi Shen, Shunyun Hu, Alexandru Iosup, and Dick Epema
The Area of Simulation Mechanism and Architecture for Multi-Avatar Virtual Environments
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), 2015, CCF B
312312 321312
Adele Lu Jia, Siqi Shen*, Ruud van de Bovenkamp, Alexandru Iosup, Fernando Kuipers, and Dick Epema
Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games
ACM Transactions on Knowledge Discovery from Data (TKDD), 2015, CCF B
312312 321312
2014
Zang Y , Huang H* , Zhang L
Efficient Structure-Aware Image Smoothingby Local Extrema on Space-Filling Curve
IEEE Transactions on Visualization & Computer Graphics
@article{2014Efficient, title={Efficient Structure-Aware Image Smoothingby Local Extrema on Space-Filling Curve}, author={ Zang, Yu and Huang, Hua and Zhang, Lei }, journal={IEEE Transactions on Visualization & Computer Graphics}, volume={20}, number={9}, pages={1253-1265}, year={2014}, }
Siqi Shen, Niels Brouwers, Alexandru Iosup, and Dick Epema
Characterization of Human Mobility in Networked Virtual Environments
NOSSDAV 2014, CCF B
Yunhua Deng, Siqi Shen, Zhe Huang, Alexandru Iosup, and Rynson Lau
Dynamic Resource Management in Cloud-based Distributed Virtual Environment
ACM Multimedia 2014, CCF A
Siqi Shen and Alexandru Iosup
Modeling Avatar Mobility of Networked Virtual Environments
MMVE 2014
Y. Zang, H. Huang, L. Zhang
Efficient Image Filtering via Local Extrema on Space Filling Curve
IEEE Transactions on Visualization and Computer Graphics
@article{zang2014efficient, title={Efficient structure-aware image smoothingby local extrema on space-filling curve}, author={Zang, Yu and Huang, Hua and Zhang, Lei}, journal={IEEE transactions on visualization and computer graphics}, volume={20}, number={9}, pages={1253--1265}, year={2014}, publisher={IEEE} }
Y. Zang, H. Huang, C. Li
Image stylization via artistic vision
The Visual Computer
@article{zang2014artistic, title={Artistic preprocessing for painterly rendering and image stylization}, author={Zang, Yu and Huang, Hua and Li, Chen-Feng}, journal={The Visual Computer}, volume={30}, number={9}, pages={969--979}, year={2014}, publisher={Springer} }
Qingyuan Zhu, Jian Yi, Shiyue Sheng, Chenglu Wen, Huosheng Hu
A computer-aided modeling and measurement system for environmental thermal comfort sensing
IEEE transactions on instrumentation and measurement
@ARTICLE{6880371, author={Zhu, Qingyuan and Yi, Jian and Sheng, Shiyue and Wen, Chenglu and Hu, Huosheng}, journal={IEEE Transactions on Instrumentation and Measurement}, title={A Computer-Aided Modeling and Measurement System for Environmental Thermal Comfort Sensing}, year={2015}, volume={64}, number={2}, pages={478-486}, doi={10.1109/TIM.2014.2345922}}
2013
Siqi Shen, Kefeng Deng, Alexandru Iosup, and Dick Epema
Scheduling Jobs in the Cloud Using On-demand and Reserved Instances
EuroPar 2013, CCF B
Siqi Shen, Alexandru Iosup, and Dick Epema
Massivizing Multi-Player Online Games on Clouds
CCGrid 2013, CCF C
Y. Zang, Hua Huang, C. Li
Stroke style analysis based image painterly rendering
Journal of Computer Science and Technology
@article{zang2013stroke, title={Stroke style analysis for painterly rendering}, author={Zang, Yu and Huang, Hua and Li, Chen-Feng}, journal={Journal of Computer Science and Technology}, volume={28}, number={5}, pages={762--775}, year={2013}, publisher={Springer} }
2011
Siqi Shen, Otto Visser, and Alexandru Iosup
RTSenv: An Experimental Environment for Real-Time Strategy Games
NetGames 2011
Siqi Shen and Alexandru Iosup
The XFire Online Meta-Gaming Network: Observation and High-Level Analysis
MMVE, 2011
2010
H. Huang, Y. Zang, C. Li
Example-Based Painting Guided by Color Features
The Visual Computer
@article{huang2010example, title={Example-based painting guided by color features}, author={Huang, Hua and Zang, Yu and Li, Chen-Feng}, journal={The Visual Computer}, volume={26}, number={6}, pages={933--942}, year={2010}, publisher={Springer} }
2009
H. Huang, Y. Zang*, Paul L. Rosin, Chun Qi
Edge-Aware Level Set Diffusion and Bilateral Filtering Reconstruction for Image Magnification
Journal of Computer Science and Technology
@article{huang2009edge, title={Edge-aware level set diffusion and bilateral filtering reconstruction for image magnification}, author={Huang, Hua and Zang, Yu and Rosin, Paul L and Qi, Chun}, journal={Journal of Computer Science and Technology}, volume={24}, number={4}, pages={734--744}, year={2009}, publisher={Springer} }