• Hi!
    I'm Jingdao

    I am an Assistant Professor in the Computer Science and Engineering Department at Mississippi State University specializing in AI, perception, and robotics. My research is centered on creating rich, versatile, and informative 3D representations of the surrounding environment through point cloud data. My research aims to leverage databases of known object models to infer structure and semantics from an unknown 3D scene. Deep learning and computational geometry techniques are used to process 3D point clouds and associate scanned objects with their corresponding design models. Real-time algorithms have been developed that enable these data parsing solutions to be deployed on robotic platforms. My research has diverse applications including autonomous driving, search and rescue, and infrastructure mapping.

Research

Drone Infrastructure Monitoring 2021

Highway infrastructure maintenance and monitoring tasks often involve labor-intensive activities and long inspection times. Examples of these maintenance tasks include landscaping and lawn care, detecting damaged road segments, and identifying missing road signs. To efficiently automate the maintenance inspection tasks, this research proposes an automated monitoring framework using Unmanned Aerial Vehicle (UAV) with artificial intelligence (AI)-driven data processing techniques. Structure from Motion (SfM) is used to create dense 3D point clouds from image data and deep learning techniques are used to segment and classify different highway assets. Point cloud-based temporal change detection is carried out with a focus on grass height estimation for monitoring highway mowing operations.

Relevant papers: [1] [2]

Disaster Site Scanning and Segmentation 2021

Disaster relief and response plays an important role in saving lives and reducing economic loss after earthquakes, windstorm events and man-made explosions. Mobile robots represent an effective solution to assist in post-disaster reconnaissance in areas that are dangerous to human agents. These robots need an accurate 3D semantic map of the site in order to carry out disaster relief work such as search and rescue and damage assessment. There exists a research need to automatically identify building elements and detect structural damage from laser-scanned points clouds acquired by mobile robots. This research proposes a learnable region growing method to perform class-agnostic point cloud segmentation in a data-driven and generalizable manner. In addition, an anomaly-based crack segmentation method is proposed where a deep feature embedding is used as a basis for separation between inlier and outlier points. Finally, an incremental segmentation scheme is used to process point cloud data in an online fashion and combine semantic information across multiple scans.

Relevant papers: [1]

Excavator 3D Workspace Monitoring 2020

A workspace visualization and pose estimation framework for teleoperated excavators. The 3D geometry of the excavator workspace is captured in real-time by a laser-scanning robot. A 3D visualization of the workspace, containing pose of end effectors, pose of salient objects, and distances between them, is continuously updated and used to provide feedback to the remote operator concerning the progress of manipulation tasks. The method was validated at a mock urban disaster site where two excavators were teleoperated to pick up and move various debris aided by our workspace visualization system.

Relevant papers: [1]

Point Cloud Object Retrieval using Deep Features 2020

A semi-automated method to efficiently retrieve duplicate building elements from raw 3D point cloud data. The point cloud is processed with a pre-trained deep feature extractor to generate a high-dimensional feature vector for each point. The selected exemplar is then provided as input to a feature-matching and peak-finding algorithm to determine positive matches. The method was validated on laser-scanned point clouds of construction sites and historical buildings.

Relevant papers: [1] [2]

Incremental Point Cloud Segmentation 2019

An online method for mobile robots to incrementally build a semantically rich 3-D point cloud of the environment. A deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The network was trained and evaluated on ray-traced scans derived from the Stanford 3-D Indoor Spaces dataset.

Relevant papers: [1]

Point Cloud Object Recognition 2019

A data-driven deep learning framework to automatically detect and classify building elements from a point cloud scene. The point cloud is first converted into a graph representation, and an edge-based classifier is used to form connected components from points in the same object. A point cloud-based object classifier is used to determine the type of building component based on the segmented points, augmented with context from surrounding points. Each detected object is matched with a corresponding (Building Element Modeling) BIM entity based on the nearest neighbor in the feature space. The method was validated on unstructured 3D point clouds of existing buildings and construction sites.

Relevant papers: [1] [2] [3]

Disaster Relief Robot Simulation 2019

A simulation framework for mobile robots to scan a disaster site and detect damaged regions based on the acquired 3D point clouds. Disaster scenarios such as earthquakes and explosions are simulated on a CAD model of a nuclear power plant using rigid body physics. 3D point clouds are obtained from the simulated sites using ray-tracing from a virtual mobile robot. The damage extent is estimated by performing point-to-point comparison between the acquired point cloud and the original 3D model.

Relevant papers: [1] [2]

Crane 3D Workspace Monitoring 2019

A multi-modal sensing and visualization framework to provide an advanced crane operation assistance system. A combination of terrestrial laser scanning and aerial photogrammetry is used to reconstruct a 3D model of the crane environment. An encoder system is used to track the base load position whereas a vision system is used to track the load swing and load rotation. The vision system is further used to detect and track moving hazards and provide collision warnings.

Relevant papers: [1] [2]

Publications

    2021

  1. Chen, J., Kira, Z. and Cho, Y. (2021).
    LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation
    IEEE Robotics and Automation Letters, 6(2), pp. 2799-2806. Accepted for oral presentation at the International Conference on Robotics and Automation (ICRA) 2021.
    [code] [bibtex]
    @ARTICLE{chen2021ral,
    title={LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation},
    author={J. {Chen} and Z. {Kira} and Y. K. {Cho}},
    journal={IEEE Robotics and Automation Letters},
    year = {2021},
    volume={6},
    number={2},
    pages={2799-2806},
    }
  2. Price, L., Chen, J., Park, J. and Cho Y. (2021).
    Multisensor-driven real-time crane monitoring system for blind lift operations: Lessons learned from a case study
    Automation in Construction, Volume 124, April 2021
    [bibtex]
    @article{price2021autcon,
    title = {Multisensor-driven real-time crane monitoring system for blind lift operations: Lessons learned from a case study},
    journal = {Automation in Construction},
    volume = {124},
    pages = {103552},
    year = {2021},
    issn = {0926-5805},
    doi = {https://doi.org/10.1016/j.autcon.2021.103552},
    url = {https://www.sciencedirect.com/science/article/pii/S0926580521000030},
    author = {Leon C. Price and Jingdao Chen and Jisoo Park and Yong K. Cho},
    }
  3. 2020

  4. Chen, J., Yi, J., Kahoush, M., Cho, E. and Cho, Y. (2020).
    Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting
    MDPI Sensors, 20(18), 5029
    [code] [bibtex]
    @Article{chen2020sensors,
    AUTHOR = {Chen, Jingdao and Yi, John Seon Keun and Kahoush, Mark and Cho, Erin S. and Cho, Yong K.},
    TITLE = {Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting},
    JOURNAL = {Sensors},
    VOLUME = {20},
    YEAR = {2020},
    NUMBER = {18},
    ARTICLE-NUMBER = {5029},
    URL = {https://www.mdpi.com/1424-8220/20/18/5029},
    ISSN = {1424-8220},
    DOI = {10.3390/s20185029}
    }
  5. Zeng, S., Chen, J., and Cho Y. (2020).
    User Exemplar-based Building Element Retrieval from Raw Point Clouds using Deep Point-level Features
    Automation in Construction, Volume 114, June 2020, 103159
    [bibtex]
    @article{zeng2020autcon,
    title = "User exemplar-based building element retrieval from raw point clouds using deep point-level features",
    journal = "Automation in Construction",
    volume = "114",
    pages = "103159",
    year = "2020",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2020.103159",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580519310908",
    author = "Shiqin Zeng and Jingdao Chen and Yong K. Cho",
    }
  6. Park, J.S., Chen, J., Cho, Y., Kang, D., and Son, B. (2020).
    CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning System.
    MDPI Sensors, 20(1), 34
    [code] [bibtex]
    @article{park2020sensors,
    AUTHOR = {Park, Jisoo and Chen, Jingdao and Cho, Yong K. and Kang, Dae Y. and Son, Byung J.},
    TITLE = {CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems},
    JOURNAL = {Sensors},
    VOLUME = {20},
    YEAR = {2020},
    NUMBER = {1},
    ARTICLE-NUMBER = {34},
    URL = {https://www.mdpi.com/1424-8220/20/1/34},
    ISSN = {1424-8220},
    DOI = {10.3390/s20010034},
    }
  7. 2019

  8. Chen, J., Cho, Y., and Kira, Z. (2019).
    Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots.
    IEEE Robotics and Automation Letters, 4(2), pp. 1240-1246,10.1109/LRA.2019.2894915
    [code] [bibtex]
    @ARTICLE{chen2019ral,
    author={J. {Chen} and Y. K. {Cho} and Z. {Kira}},
    journal={IEEE Robotics and Automation Letters},
    title={Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots},
    year={2019},
    volume={4},
    number={2},
    pages={1240-1246},
    }
  9. Chen, J., Kira, Z., and Cho, Y. (2019).
    Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction.
    ASCE Journal of Computing in Civil Engineering, 33(4) DOI:10.1061/(ASCE)CP.1943-5487.0000842
    [bibtex]
    @article{chen2019jcce,
    author = {Jingdao Chen and Zsolt Kira and Yong K. Cho },
    title = {Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction},
    journal = {Journal of Computing in Civil Engineering},
    volume = {33},
    number = {4},
    pages = {04019027},
    year = {2019},
    doi = {10.1061/(ASCE)CP.1943-5487.0000842},
    }
  10. 2018

  11. Fang, Y., Chen, J., Cho, Y., and Kim, K.N. (2018).
    Vision-based Load Sway Monitoring to Improve Crane Safety in Blind Lifts.
    Journal of Structural Integrity and Maintenance, 10.1080/24705314.2018.1531348
    [bibtex]
    @article{fang2018jsim,
    author = {Yihai Fang and Jingdao Chen and Yong K. Cho and Kinam Kim and Sijie Zhang and Esau Perez},
    title = {Vision-based load sway monitoring to improve crane safety in blind lifts},
    journal = {Journal of Structural Integrity and Maintenance},
    volume = {3},
    number = {4},
    pages = {233-242},
    year = {2018},
    publisher = {Taylor & Francis},
    doi = {10.1080/24705314.2018.1531348},
    }
  12. Kim, P., Chen, J., and Cho, Y. (2018).
    SLAM-driven robotic mapping and registration of 3D point clouds.
    Automation in Construction, doi.org/10.1016/j.autcon.2018.01.009
    [bibtex]
    @article{kim2018autcon,
    title = "SLAM-driven robotic mapping and registration of 3D point clouds",
    journal = "Automation in Construction",
    volume = "89",
    pages = "38 - 48",
    year = "2018",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2018.01.009",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580517303990",
    author = "Pileun Kim and Jingdao Chen and Yong K. Cho",
    }
  13. Chen, J., Fang, Y., and Cho, Y. (2018).
    Performance Evaluation of 3D Descriptors for Object Recognition in Construction Applications.
    Automation in Construction, Volume 86,February 2018, Pages 44-52, DOI: 10.1016/j.autcon.2017.10.033
    [bibtex]
    @article{chen2018autcon,
    title = "Performance evaluation of 3D descriptors for object recognition in construction applications",
    journal = "Automation in Construction",
    volume = "86",
    pages = "44 - 52",
    year = "2018",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2017.10.033",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580517303862",
    author = "Jingdao Chen and Yihai Fang and Yong K. Cho",
    }
  14. Kim, P., Chen, J., and Cho, Y. (2018).
    Automated Point Clouds Registration using Visual and Planar Features for Construction Environments.
    ASCE Journal of Computing in Civil Engineering, Volume 32, Issue2, March 2018, DOI: 10.1061/(ASCE)CP.1943-5487.0000720
    [bibtex]
    @article{kim2018jcce,
    author = {Pileun Kim and Jingdao Chen and Yong K. Cho },
    title = {Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments},
    journal = {Journal of Computing in Civil Engineering},
    volume = {32},
    number = {2},
    pages = {04017076},
    year = {2018},
    doi = {10.1061/(ASCE)CP.1943-5487.0000720}
    }
  15. 2017

  16. Kim, P., Chen, J., and Cho, Y. (2017).
    Robotic sensing and object recognition from thermal-mapped point clouds.
    International Journal of Intelligent Robotics and Applications. September 2017, Volume 1, Issue 3, Pages 243-254, DOI: 10.1007/s41315-017-0023-9
    [bibtex]
    @Article{kim2017ijira,
    author="Kim, Pileun
    and Chen, Jingdao
    and Cho, Yong K.",
    title="Robotic sensing and object recognition from thermal-mapped point clouds",
    journal="International Journal of Intelligent Robotics and Applications",
    year="2017",
    month="Sep",
    day="01",
    volume="1",
    number="3",
    pages="243--254",
    }
  17. Chen, J., Fang, Y., and Cho, Y. (2017).
    Real-Time 3D Crane Workspace Update Using a Hybrid Visualization Approach.
    ASCE Journal of Computing in Civil Engineering, Volume 31, Issue 5, DOI: 10.1061/(ASCE)CP.1943-5487.0000698
    [bibtex]
    @article{chen2017jcce,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Real-Time 3D Crane Workspace Update Using a Hybrid Visualization Approach},
    journal = {Journal of Computing in Civil Engineering},
    volume = {31},
    number = {5},
    pages = {04017049},
    year = {2017},
    doi = {10.1061/(ASCE)CP.1943-5487.0000698},
    }
  18. Park, J.W., Chen, J., and Cho, Y. (2017).
    Self-Corrective Knowledge-based Hybrid Tracking System Using BIM and Multimodal Sensors.
    Advanced Engineering Informatics, Volume 32, Issue C, April 2017, Pages 126-138, DOI: 10.1016/j.aei.2017.02.001
    [bibtex]
    @article{park2017aei,
    title = "Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors",
    journal = "Advanced Engineering Informatics",
    volume = "32",
    pages = "126 - 138",
    year = "2017",
    issn = "1474-0346",
    doi = "https://doi.org/10.1016/j.aei.2017.02.001",
    url = "http://www.sciencedirect.com/science/article/pii/S147403461630252X",
    author = "JeeWoong Park and Jingdao Chen and Yong K. Cho",
    }
  19. Chen, J., Fang, Y., Cho, Y., Kim, C. (2017).
    Principal Axes Descriptor (PAD) for Automated Construction Equipment Classification from Point Clouds.
    ASCE's Journal of Computing in Civil Engineering, Volume 31, Issue 2, March 2017, DOI: 10.1061/(ASCE)CP.1943-5487.0000628
    [bibtex]
    @article{chen2017pad,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho and Changwan Kim },
    title = {Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds},
    journal = {Journal of Computing in Civil Engineering},
    volume = {31},
    number = {2},
    pages = {04016058},
    year = {2017},
    doi = {10.1061/(ASCE)CP.1943-5487.0000628}
    }
  20. 2016

  21. Fang, Y.,Cho, Y., and Chen, J. (2016).
    A Framework for Real-time Pro-active Safety Assistance for Mobile Crane Lifting Operations.
    Automation in Construction, Volume 72, Part 3, December 2016, Pages 367-379, DOI: 10.1016/j.autocon.2016.08.025
    [bibtex]
    @article{fang2016autcon,
    title = "A framework for real-time pro-active safety assistance for mobile crane lifting operations",
    journal = "Automation in Construction",
    volume = "72",
    pages = "367 - 379",
    year = "2016",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2016.08.025",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580516301807",
    author = "Yihai Fang and Yong K. Cho and Jingdao Chen",
    }

    2021

  1. Kahoush, M., Yajima, Y., Kim, S., Chen, J., Park, J., Kangisser, S., Irizarry, J., Cho,Y. (2021).
    Analysis of Flight Parameters on UAV Semantic Segmentation Performance for Highway Infrastructure Monitoring.
    Proceedings of the ASCE 2021 International Conference on Computing in Civil Engineering (i3CE), Orlando, FL, USA, September 12-14
    [bibtex]
  2. Yajima, Y., Kahoush, M., Kim, S., Chen, J., Park, J., Kangisser, S., Irizarry, J., Cho,Y. (2021).
    AI-driven 3D Point Cloud-Based Highway Infrastructure Monitoring System using UAV.
    Proceedings of the ASCE 2021 International Conference on Computing in Civil Engineering (i3CE), Orlando, FL, USA, September 12-14
    [bibtex]
  3. 2020

  4. Chen, J., Kim, P., Sun, D.I., Han, C.S., Ahn, Y.H., Ueda, J. and Cho, Y. (2020).
    Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds.
    Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), Kitakyshu, Japan, October 27-28
    [bibtex]
    @INPROCEEDINGS{chen2020isarc,
    author={Jingdao Chen and Pileun Kim and Dong-Ik Sun and Chang-Soo Han and Yong-Han Ahn and Jun Ueda and Yong K. Cho},
    booktitle={37th International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds},
    year={2020},
    month={October},
    pages={781-788},
    }
  5. Price, L., Chen, J., and Cho, Y. (2020).
    Dynamic Crane Workspace Update for Collision Avoidance during Blind Lift Operations.
    Proceedings of the 18th International Conference on Computing in Civil and Building Engineering, ICCCBE, pp. 959-970, S√£o Paulo, Brazil
    [bibtex]
    @InProceedings{price2020,
    author="Price, Leon C. and Chen, Jingdao and Cho, Yong K.",
    editor="Toledo Santos, Eduardo and Scheer, Sergio",
    title="Dynamic Crane Workspace Update for Collision Avoidance During Blind Lift Operations",
    booktitle="Proceedings of the 18th International Conference on Computing in Civil and Building Engineering",
    year="2020",
    publisher="Springer International Publishing",
    address="Cham",
    pages="959--970",
    isbn="978-3-030-51295-8",
    }
  6. Chen, J., and Cho, Y. (2020).
    Unsupervised Crack Segmentation from Disaster Site Point Clouds using Point Feature Clustering.
    Proceedings of Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE, pp. 125-133, Berlin, Germany
    [bibtex]
    @inproceedings {chen2020egice,
    author = {Chen, Jingdao and Cho, Yong},
    editor = {Ungureanu, Lucian Constantin AND Hartmann, Timo},
    title = {Unsupervised Crack Segmentation from Disaster Site Point Clouds using Point Feature Clustering},
    booktitle = {EG-ICE 2020 Workshop on Intelligent Computing in Engineering},
    year = {2020},
    publisher = {Universitätsverlag der TU Berlin},
    address = {Berlin},
    doi = {10.14279/depositonce-9977},
    url = {http://dx.doi.org/10.14279/depositonce-9977},
    pages = {125-133},
    }
  7. Park, J., Chen, J., and Cho Y. (2020).
    Point Cloud Information Modeling (PCIM): an Innovative Framework for as-is Information Modeling of Construction Sites
    ASCE Construction Research Congress (CRC) 2020, March 9-10, Tempe, AZ.
    [bibtex]
    @inproceedings{park2020crc,
    author = {Jisoo Park and Jingdao Chen and Yong K. Cho },
    title = {Point Cloud Information Modeling (PCIM): an Innovative Framework for as-is Information Modeling of Construction Sites},
    booktitle = {Construction Research Congress 2020},
    year = {2020},
    }
  8. 2019

  9. Chen, J., and Cho Y. (2019).
    Exemplar-based Building Element Retrieval from Point Clouds
    International Conference on Smart Infrastructure and Construction (ICSIC), Churchill College, Cambridge, UK, July 8-9.
    [bibtex]
    @inproceedings{chen2019icsic,
    author = {Jingdao Chen and Yong Cho},
    title = {Exemplar-Based Building Element Retrieval from Point Clouds},
    booktitle = {International Conference on Smart Infrastructure and Construction 2019 (ICSIC)},
    year = "2019",
    pages = {225-231},
    doi = {10.1680/icsic.64669.225},
    URL = {https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.225},
    }
  10. Chen, J. and Cho, Y. (2019).
    Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots.
    IEEE 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Korea, June 24-27
    [bibtex]
    @INPROCEEDINGS{chen2019ur,
    author={J. {Chen} and Y. K. {Cho}},
    booktitle={2019 16th International Conference on Ubiquitous Robots (UR)},
    title={Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots},
    year={2019},
    pages={648-653},
    }
  11. Chen, J., Kim, K.N., Cho,Y., Lee, J., Kim, B., Ahn, Y., and Kang, J. (2019).
    Nuclear Power Plant Disaster Site Simulation using Rigid Body Physics.
    Proceedings of the ASCE 2019 International Conference on Computing in Civil Engineering (i3CE), Atlanta, GA, USA, June 17-19, DOI:10.1061/9780784482421.069
    [bibtex]
    @inproceedings{chen2019i3ce,
    author = {Jingdao Chen and Kinam Kim and Yong K. Cho and Joo Sung Lee and Byeol Kim and Yong Han Ahn and Junsuk Kang },
    title = {Nuclear Power Plant Disaster Site Simulation Using Rigid Body Physics},
    booktitle = {International Conference on Computing in Civil Engineering 2019},
    year={2019},
    pages = {546-552},
    doi = {10.1061/9780784482421.069},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784482421.069},
    }
  12. Kim, K.N., Chen, J., and Cho, Y. (2019).
    Evaluation of Machine Learning Algorithms for Worker's Motion Recognition using Motion Sensors.
    Proceedings of the ASCE 2019 International Conference on Computing in Civil Engineering (i3CE), Atlanta, GA, USA, June 17-19, DOI:10.1061/9780784482438.007
    [bibtex]
    @inproceedings{kim2019i3ce,
    author = {Kinam Kim and Jingdao Chen and Yong K. Cho },
    title = {Evaluation of Machine Learning Algorithms for Worker's Motion Recognition Using Motion Sensors},
    booktitle = {International Conference on Computing in Civil Engineering 2019},
    year={2019},
    pages = {51-58},
    doi = {10.1061/9780784482438.007},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784482438.007},
    }
  13. 2018

  14. Chen, J., Kim, P., Cho, Y., and Ueda, J. (2018).
    Object-sensitive potential fields for mobile robot navigation and mapping in indoor environments.
    Proceedings of the 2018 IEEE 15th International Conference on Ubiquitous Robots (UR), Honolulu, HI, USA, June 26-30, 10.1109/URAI.2018.8441896
    [bibtex]
    @INPROCEEDINGS{chen2018ur,
    author={J. {Chen} and P. {Kim} and Y. K. {Cho} and J. {Ueda}},
    booktitle={2018 15th International Conference on Ubiquitous Robots (UR)},
    title={Object-sensitive potential fields for mobile robot navigation and mapping in indoor environments},
    year={2018},
    pages={328-333},
    doi={10.1109/URAI.2018.8441896},
    month={June},
    }
  15. Chen, J., Cho, Y., and Ueda, J. (2018).
    Sampled-Point Network for Classification of Deformed Building Element Point Clouds.
    Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), BrisBane, Australia, May 21-25, 10.1109/ICRA.2018.8461095
    [code] [bibtex]
    @INPROCEEDINGS{chen2018icra,
    author={J. {Chen} and Y. K. {Cho} and J. {Ueda}},
    booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
    title={Sampled-Point Network for Classification of Deformed Building Element Point Clouds},
    year={2018},
    pages={2164-2169},
    }
  16. Fang, Y., Chen, J., Cho, Y., Zhang, S., and Perez, E. (2018).
    Enhance Blind Lift Safety by Real-Time Sensing and Visualization.
    Proceedings of the 18th International Conference on Construction Applications of Virtual Reality (CONVR2018), Auckland, New Zealand, Nov 22-23
    [bibtex]
    @INPROCEEDINGS{fang2018convr,
    author={Yihai Fang and Jingdao Chen and Yong Cho and Sijie Zhang and Esau Perez},
    booktitle={18th International Conference on Construction Applications of Virtual Reality (CONVR)},
    title={Enhance Blind Lift Safety by Real-Time Sensing and Visualization},
    year={2018},
    month={November},
    }
  17. Kim, P., Chen, J., Kim, J., and Cho, Y. (2018).
    SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications.
    Proceedings of Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE,. pp. 254-269, Lausanne, Switzerland, DOI: 10.1007/978-3-319-91635-4_14
    [bibtex]
    @InProceedings{kim2018egice,
    author="Kim, Pileun and Chen, Jingdao and Kim, Jitae and Cho, Yong K.",
    editor="Smith, Ian F. C. and Domer, Bernd",
    title="SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications",
    booktitle="Advanced Computing Strategies for Engineering",
    year="2018",
    publisher="Springer International Publishing",
    pages="254--269",
    }
  18. Chen, J. and Cho, Y. (2018).
    Point-to-point Comparison Method for Automated Scan-vs-BIM Deviation Detection.
    Proceedings of 17th International Conference on Computing in Civil and Building Engineering, Tampere, Finland, June 4-7.
    [bibtex]
    @INPROCEEDINGS{chen2018icccbe,
    author={Jingdao Chen and Yong Cho},
    booktitle={17th International Conference on Computing in Civil and Building Engineering},
    title={Point-to-point Comparison Method for Automated Scan-vs-BIM Deviation Detection},
    year={2018},
    month={June},
    }
  19. Kim, P., Chen, J., Cho, Y. (2018).
    Autonomous Mobile Robot Localization and Mapping for Unknown Construction Environments.
    ASCE Construction Research Congress (CRC) 2018, pp.147-156, April 2-4, New Orleans, LA, DOI: 10.1061/9780784481264.015
    [bibtex]
    @inproceedings{kim2018crc,
    author = {Pileun Kim and Jingdao Chen and Yong K. Cho },
    title = {Autonomous Mobile Robot Localization and Mapping for Unknown Construction Environments},
    booktitle = {Construction Research Congress 2018},
    year = {2018},
    pages = {147-156},
    doi = {10.1061/9780784481264.015},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784481264.015},
    }
  20. Chen, J., Cho, Y., and Kim, K. (2018).
    Region Proposal Mechanism for Building Element Recognition for Advanced Scan-to-BIM Process.
    ASCE Construction Research Congress 2018,April2-4, New Orleans, LA, Doi: 10.1061/9780784481264.022
    [bibtex]
    @inproceedings{chen2018crc,
    author = {Jingdao Chen and Yong K. Cho and Kyungki Kim },
    title = {Region Proposal Mechanism for Building Element Recognition for Advanced Scan-to-BIM Process},
    booktitle = {Construction Research Congress 2018},
    year = {2018},
    pages = {221-231},
    doi = {10.1061/9780784481264.022},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784481264.022},
    }
  21. 2017

  22. Kim, P., Chen, J., and Cho, Y. (2017).
    Building element recognition with thermal-mapped point clouds.
    Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC), Taipei, Taiwan, June 28-July 1, DOI: 10.22260/ISARC2017/0122
    [bibtex]
    @INPROCEEDINGS{kim2017isarc,
    author={Pileun Kim and Jingdao Chen and Yong Cho},
    booktitle={34th International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Building element recognition with thermal-mapped point clouds},
    year={2017},
    month={June},
    }
  23. Chen, J., Fang, Y., and Cho, Y. (2017).
    Mobile Asset Tracking for Dynamic 3D Crane Workspace Generation in Real Time.
    Proceedings of the 2017 International Workshop on Computing for Civil Engineering (IWCCE), Seattle, WA, USA, June 25-27, DOI: 10.1061/9780784480830.016
    [bibtex]
    @inproceedings{chen2017crane,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Mobile Asset Tracking for Dynamic 3D Crane Workspace Generation in Real Time},
    booktitle = {International Workshop on Computing in Civil Engineering 2017},
    year = {2017},
    pages = {122-129},
    doi = {10.1061/9780784480830.016},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784480830.016},
    }
  24. Chen, J., Fang, Y., and Cho, Y. (2017).
    Unsupervised Recognition of Volumetric Structural Components from Building Point Clouds.
    Proceedings of the 2017 International Workshop on Computing for Civil Engineering (IWCCE), Seattle, WA, USA, June 25-27, DOI: 10.1061/9780784480823.005
    [bibtex]
    @inproceedings{chen2017iwcce,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Unsupervised Recognition of Volumetric Structural Components from Building Point Clouds},
    booktitle = {International Workshop on Computing in Civil Engineering 2017},
    year = {2017},
    pages = {34-42},
    doi = {10.1061/9780784480823.005},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784480823.005},
    }
  25. 2016

  26. Kim, P., Cho, Y., and Chen, J. (2016).
    Automatic Registration of Laser Scanned Color Point Clouds Based on Common Feature Extraction.
    16th International Conference on Construction Applications of Virtual Reality (CONVR), Hong Kong, Dec. 11-13
    [bibtex]
    @INPROCEEDINGS{chen2016convr,
    author={Pileun Kim and Yong Cho and Jingdao Chen},
    booktitle={16th International Conference on Construction Applications of Virtual Reality (CONVR)},
    title={Automatic Registration of Laser Scanned Color Point Clouds Based on Common Feature Extraction},
    year={2016},
    month={December},
    }
  27. Chen, J., Fang, Y., and Cho, Y. (2016).
    Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0027
    [bibtex]
    @INPROCEEDINGS{chen2016isarc,
    author={Jingdao Chen and Yihai Fang and Yong Cho},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management},
    year={2016},
    month={July},
    }
  28. Chen, J. and Cho, Y. (2016).
    Real-time 3D Mobile Mapping for the Built Environment
    . International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0028
    [bibtex]
    @INPROCEEDINGS{chen2016slam,
    author={Jingdao Chen and Yong Cho},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Real-time 3D Mobile Mapping for the Built Environment},
    year={2016},
    month={July},
    }
  29. Fang, Y., Chen, J., Cho, Y., and Zhang, P. (2016).
    A Point Cloud-Vision Hybrid Approach for 3D Location Tracking of Mobile Construction Assets.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0074
    [bibtex]
    @INPROCEEDINGS{fang2016isarc,
    author={Yihai Fang and Jingdao Chen and Yong Cho and Peiyao Zhang},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={A Point Cloud-Vision Hybrid Approach for 3D Location Tracking of Mobile Construction Assets},
    year={2016},
    month={July},
    }
  30. Kim, P., Cho, Y. Chen, J. (2016).
    Target-Free Automatic Registration of Point Clouds.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0083
    [bibtex]
    @INPROCEEDINGS{kim2016isarc,
    author={Pileun Kim and Yong Cho and Jingdao},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Target-Free Automatic Registration of Point Clouds},
    year={2016},
    month={July},
    }

    2021

  1. Chen, J., Park, J., Yajima, Y., Kim, S. (2021).
    GTS2B
    1st Workshop and Challenge on Computer Vision in the Built Environment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

Teaching

Courses at Mississippi State University:
  • Fall 2021 - CSE 4643/6643: AI Robotics

Code

Learnable Region Growing

Code to perform class-agnostic 3D point cloud segmentation using a learnable region growing method. Implemented in Tensorflow.

Infrared Segmentation

Code to perform person detection by semantic segmentation from night-time infrared (IR) images. Implemented in Tensorflow.

Multi-view Incremental Segmentation

Code to incrementally perform semantic instance segmentation of laser-scanned 3D point clouds. Implemented in ROS + Tensorflow.

Point Cloud Scene Completion

Code to perform scene completion of obstructed building facades using generative adversarial inpainting. Implemented in Tensorflow.

Python Coding Exercises

A series of intro-level Python coding exercises for scientific computing applications.

Interests

I enjoy playing various sports including soccer and badminton. I play several musical instruments and am an avid listener of classical music and jazz music.