Libo Sun

Libo Sun

Grant-Funded Researcher (B)

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

Libo Sun is currently a grant-funded researcher at The University of Adelaide. Before this, he worked as a Ph.D. researcher at the Australian Centre for Robotic Vision (ACRV) and the Australian Institute for Machine Learning (AIML) in Australia, as well as a research associate at Nanyang Technological University (NTU) in Singapore. His research mainly focuses on the intersection of robotic vision and deep learning. Specifically, his research interests include, but are not limited to, depth estimation, 3D reconstruction, and view synthesis. His first-author papers, which summarize his primary research topics and outputs, have been published in the top-tier robotics journal T-RO and the machine intelligence journal T-PAMI. He serves as a reviewer for nearly all major robotics and computer vision conferences.

  • Journals
    Year Citation
    2024 Sun, L., Bian, J. W., Zhan, H., Yin, W., Reid, I., & Shen, C. (2024). SC-DepthV3: Robust Self-Supervised Monocular Depth Estimation for Dynamic Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1), 497-508.
    DOI Scopus1
    2023 Sun, L., Bockman, J., & Sun, C. (2023). A Framework for Leveraging Interimage Information in Stereo Images for Enhanced Semantic Segmentation in Autonomous Driving. IEEE Transactions on Instrumentation and Measurement, 72, 1-12.
    DOI Scopus1
    2022 Sun, L., Zhang, H., & Yin, W. (2022). Pseudo-LiDAR Based Road Detection. IEEE Transactions on Circuits and Systems for Video Technology, 32(8), 5386-5398.
    DOI Scopus5 WoS2
    2022 Sun, L., Yin, W., Xie, E., Li, Z., Sun, C., & Shen, C. (2022). Improving Monocular Visual Odometry Using Learned Depth. IEEE Transactions on Robotics, 38(5), 3173-3186.
    DOI Scopus5 WoS3
  • Position: Grant-Funded Researcher (B)
  • Email: libo.sun@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 2
  • Org Unit: Australian Institute for Machine Learning - Operations
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