Erkang Cheng

Erkang Cheng is a computer scientist working on computer vision and autonomous driving. He received Ph.D. from Temple University, USA, in 2014 supervised by Prof. Haibin Ling. His research interests include computer vision, deep learning, autonomous driving, and medical image analysis. His work has been published in several top venues including CVPR, ICCV, MICCAI, ICRA and IROS.

His current focus is to solve autonomous perception problems and fundamental research in medical image analysis.

Email: twokang dot cheng AT gmail dot com. Google Scholar

profile photo

News

  • [01/2023] NEW One paper of 3D lane detection gets accepted to ICRA 2023.
  • [11/2022] One paper gets accepted to SPIE Medical Imaging 2023.
  • [09/2022] One paper of BEV semantic segmentation gets accepted to WACV 2023.
  • [07/2022] Invited Talk at Autobitxyz (汽车之心-行家说) on BEV + Transformer for Perception of Autonomous Driving. [video] [pdf]
  • PrePrint

    CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
    Yifeng Bai^, Zhirong Chen^, Zhangjie Fu, Lang Peng, Pengpeng Liang, Erkang Cheng*
    ICRA, 2023 (accepted)
    [paper] [video]

    Research

    *Corresponding author. ^Equal contribution.

    CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
    Yifeng Bai^, Zhirong Chen^, Zhangjie Fu, Lang Peng, Pengpeng Liang, Erkang Cheng*
    ICRA, 2023 (accepted)
    [paper] [video]
    Distance Transform Guided Medical Image Segmentation with Channel Information Exchange
    Han Yang, Bin Cai, Pengpeng Liang, Zhiyong Sun, Bo Song, Erkang Cheng*
    SPIE Medical Imaging, 2023 (accepted)
    BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs
    Lang Peng, Zhirong Chen, Zhangjie Fu, Pengpeng Liang, Erkang Cheng*
    WACV, 2023
    [paper] [video]
    Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving
    Naifan Li, Fan Song, Ying Zhang, Pengpeng Liang, Erkang Cheng*
    ICRA, 2022
    [paper] [dataset]
    Pseudo Segmentation for Semantic Information-Aware Stereo Matching
    Shengyou Hua, Zhiyong Sun, Bo Song, Pengpeng Liang, Erkang Cheng
    IEEE Signal Processing Letters, 2022
    Accurate Preoperative Path Planning with Coarse-to-refine Segmentation for Image Guided Deep Brain Stimulation
    Bin Cai, Chi Xiong, Zhiyong Sun, Pengpeng Liang, Kaifeng Wang, Yuhao Guo, Chaoshi Niu, Bo Song, Erkang Cheng*, Xiongbiao Luo
    Biomedical Signal Processing and Control, 2022
    Orthographic Pooling: Learned Maximum Intensity Projection for Vertebrae Labelling
    Bin Cai, Yuhao Guo, Pengpeng Liang, Kaifeng Wang, Zhiyong Sun, Chi Xiong, Bo Song, Chaoshi Niu, Erkang Cheng*
    EMBC, 2022
    Efficient Network with Ghost Tied Block for Heart Segmentation
    Yuhao Guo, Bin Cai, Pengpeng Liang, Kaifeng Wang, Zhiyong Sun, Chi Xiong, Bo Song, Chaoshi Niu, Erkang Cheng*
    SPIE Medical Imaging, 2022
    Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes
    Chengcheng Guo, Minjie Lin, Heyang Guo, Pengpeng Liang, Erkang Cheng*
    IROS, 2021
    [paper] [video]
    Learning Local Descriptors with Multi-level Feature Aggregation and Spatial Context Pyramid
    Pengpeng Liang, Haoxuanye Ji, Erkang Cheng, Yumei Chai, Liming Wang, Haibin Ling
    Neurocomputing, 2021
    Joint Spinal Centerline Extraction and Curvature Estimation with Row-Wise Classification and Curve Graph Network
    Long Huo, Bin Cai, Pengpeng Liang, Zhiyong Sun, Chi Xiong, Chaoshi Niu, Bo Song, Erkang Cheng*
    MICCAI, 2021
    Ghost-light-3dnet: Efficient Network for Heart Segmentation
    Bin Cai^, Erkang Cheng^, Pengpeng Liang, Chi Xiong, Zhiyong Sun, Qiang Zhang, Bo Song
    ISBI, 2021

    Talk

  • [07/2022] Invited Talk at Autobitxyz (汽车之心-行家说) on BEV + Transformer for Perception of Autonomous Driving. [video] [pdf]