Juil Sock

PhD student in Imperial Computer Vision & Learning Lab

 

Email

ju-il.sock08@imperial.ac.uk

 

Address

Room 1008d, Imperial Computer Vision and Learning Lab 
Department of Electrical and Electronic Engineering 
Imperial College London 
London, SW7 2BT, UK

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I am a PhD student in Imperial Computer Vision & Learning Lab. My work is supervised by Dr. Tae-Kyun Kim. My research interests are in machine learning and computer vision problems. I am currently working on object instance detection and pose estimation in a crowded scene.

I completed a MEng degree at Imperial College London in 2012 under the supervision of Dr. Tae-Kyun Kim. Following that, I spent 4 year at the Agency for Defense Development where I worked on development of autonomous ground vehicle on rough terrain using various sensors.

News
 
  • July 2020 - One paper accepted at International Conference on Intelligent Robots and Systems (IROS'20)

  • March 2020 - 'Tackling Two Challenges of 6D Object Pose Estimation: Lack of Real Annotated RB Images and Scalability to Number of Objects' uploaded to ArXiv.

  • December 2019 - I have co-authored a book chapter in "RGB-D Image Analysis and Processing" 

  • October 2019 - I am co-organizing 5th International Workshop on Recovering 6D Object Pose at ICCV'19

  • July 2018 - One paper accepted at British Machine Vision Conference(BMVC'18)

  • December 2017 - One paper accepted for an oral presentation at Association for the Advancement of Artificial Intelligence(AAAI'18)

  • October 2017 - I will be a co-organiser of the 3rd International Workshop on Recovering 6D Object Pose at ICCV'17

  • September 2017 - One paper accepted for an oral presentation at the 3rd International Workshop on Recovering 6D Object Pose at ICCV'17

  • September 2017 - Student volunteer at BMVC 2017

  • July 2017 - One paper accepted to ICCV 2017

  • October 2016 - Started PhD. program at Imperial College

  • June 2016 - A journal paper published to Sensors

  • March 2016 - One paper accepted to ICRA 2016

Publications
  • J. Sock, P. Castro, A. Armagan, G. Garcia-Hernando, T-K Kim, Tackling Two Challenges of 6D Object Pose Estimation: Lack of Real Annotated RB Images and Scalability to Number of Objects, ArXiv [PDF]

  • J. Sock, G. Garcia-Hernando, T-K. Kim, Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning, Proc. of International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, Oct 2020 [PDF]

  • C. Sahin, G. Garcia-Hernando, J. Sock, T-K. Kim, Instance- and Category-level 6D Object Pose Estimation, book chapter in RGB-D Image Analysis and Processing, Springer, 2019

  • J. Sock, K.I. Kim, C. Sahin, T-K. Kim, Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios, Proc. of British Machine Vision Conference (BMVC), Newcastle upon Tyne, UK, Sep 2018 [PDF]

  • Hamidreza Kasaei, Juil Sock, Luis Seabra Lopes, Ana Maria Tome, T-K Kim, Perceiving, Learning, and Recognizing 3D Objects : An Approach to Cognitive Service Robots, Proc. of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, Feb 2018 [PDF]

  • Juil Sock, S.Hamidreza Kasaei, Luis Seabra Lopes, T-K Kim, Multi-view 6D Object Pose Estimation and Camera Motion Planning using RGBD Images, Proc. of Internation Conference on Computer Vision Workshop (ICCVW), Venice, Italy, Oct 2017 [PDF]

  • Vassileios Balntas, Andreas Doumanoglou, Caner Sahin, Juil Sock, Rigas Kouskouridas, T-K Kim, Pose Guided RGBD Feature Learning for 3D Object Pose Estimation,  Proc. of International Conference on Computer Vision (ICCV), Venice, Italy, Oct 2017 [PDF]

  • Juil Sock, Jun Kim, Jihong Min, Kiho Kwak, Probabilistic traversability map generation using 3D-LIDAR and camera, Proc. of International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 2016 [Link]

  • Sungdae Sim, Juil Sock, Kiho Kwak, Indirect Correspondence-Based Robust Extrinsic Calibration of LiDAR and Camera.  IEEE Sensors Journal,  2016 [PDF]

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