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Computer Vision Group
Faculty of Informatics
Technical University of Munich

Technical University of Munich

Home Members Rui Wang

Rui Wang


Technische Universität München

Department of Computer Science
Informatik 9
Boltzmannstrasse 3
85748 Garching

Tel: +49-89-289-17759
Fax: +49-89-289-17757
Office: 02.09.044
Mail: rui.wang@in.tum.de


  • Jun - Dec 2018: I will be interning with Dieter Fox in the recently founded Nvidia Robotics Research Lab in Seattle.
  • May 22 2018: We have released our code for online photometric calibration! Please find the link on the Project Page. The paper was recently nominated by ICRA'18 for the Best Vision Paper Award.

Brief Bio

I received my Bachelor's degree (2011) in Automation from Xi'an Jiaotong University, and my Master's degree (2014) in Electrical Engineering and Information Technology from the Technical University of Munich. From 2014 to 2016 I worked as a computer vision algorithm developer for advanced driver assistance systems (ADAS) at Continental. Since March 2016 I am a PhD student in the Computer Vision Group at the Technical University of Munich, headed by Professor Daniel Cremers. My research interests include visual SLAM and visual 3D reconstruction, as well as their combinations with semantic information.

Find me on Google Scholar, LinkedIn.

Research Interests

Visual Semantic 3D Reconstruction

Visual Odometry / SLAM

  • Stereo DSO This video shows some results of our paper “Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras” accepted by ICCV 2017. (Project Page)

  • SLAM extension to Stereo DSO After the ICCV 2017 deadline, we extended our method to a SLAM system with additional components for map maintenance, loop detection and loop closure. Our performance on KITTI is further boosted a little, as shown by the plots in the video. (Project Page)

  • Online Photometric Calibration We've conducted a project to achieve online photometric calibration, where the exposure times of consecutive frames, the camera response function, and the camera vignetting factors can be recovered in real-time. Experiments show that our estimations converge to the ground truth after only a few seconds. Our approach can be used either offline for calibrating existing datasets, or online in combination with state-of-the-art direct visual odometry or SLAM pipelines. For more details please check our paper “Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM”. (Project Page)

Master Theses / IDP / Guided Research

Please send me your transcripts and CV by email.



Journal Articles
Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang, D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018.(This paper was also selected by ICRA'18 for presentation at the conference.[arxiv][video][code]) [bib] [pdf]ICRA'18 Best Vision Paper Award - Finalist
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect (N. Yang, R. Wang, X. Gao, D. Cremers), In IEEE Robotics and Automation Letters (RA-L), 2018.([arxiv]) [bib] [pdf]
Conference and Workshop Papers
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (R. Wang, M. Schwörer, D. Cremers), In International Conference on Computer Vision (ICCV), 2017.([supplementary][video][arxiv]) [bib] [pdf]
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Informatik IX
Chair for Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching