Direkt zum Inhalt springen
Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

Menu
Home Members Rui Wang

Rui Wang

PhD StudentTechnische Universität München

Department of Computer Science
Informatik 9
Boltzmannstrasse 3
85748 Garching
Germany

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

News

  • [10.2018] The code for LDSO (Direct Sparse Odometry with Loop Closure) has been released! Please visit the project page for details.
  • [07.2018] We have one paper accepted by ECCV'18 (oral) and two papers accepted by IROS'18.
  • [06-12.2018] I will be interning with Prof. Dieter Fox in the recently founded Nvidia Robotics Research Lab in Seattle.
  • [05.2018] 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.
  • [03.2018] I join Artisense, a startup co-founded by Prof. Daniel Cremers, as a PhD Student and Senior Computer Vision & AI Researcher.

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, Strava.

Research Interests

Semantic VO / SLAM

  • Direct Shape This video shows the basic idea and some results of our paper "DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation". In this work, we estimate the 3D poses and shapes of cars based on a single stereo image pair. Note that the point clouds in the video are only for visualization purpose, they are not used in our method. For more details please refer to the paper arXiv. (The project page is under construction.)


Classic VO / 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)


  • LDSO: Direct Sparse Odometry with Loop Closure In this project we integrate feature points into DSO to improve the repeatability of the sampled points, enabling loop closure candidate detection using BoW and loop closure based on pose graph optimization. For more details and access to the code, please visit the Project Page.


Deep Learning Boosted VO / SLAM

  • Deep Virtual Stereo Odometry (DVSO) In this project we design a novel deep network and train it in a semi-supervised way to predict depth map from single image, and integrate the depth map into DSO as virtual stereo measurement. Being a monocular VO approach, DVSO achieves comparable performance to the state-of-the-art stereo methods. (Project Page)


Camera Calibration

  • 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.

Teaching

Service

Reviewer: CVPR, ICCV, ICRA, IROS, RA-L

Publications


Export as PDF, TEX or BIB

Journal Articles
2018
[]Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang and 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][project]) [bibtex] [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 and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. (This paper was also selected by IROS'18 for presentation at the conference.[arxiv]) [bibtex] [doi] [pdf]
Preprints
2019
[]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In preprint, 2019. ([arxiv][video] Project page is under construction.) [bibtex]
Conference and Workshop Papers
2018
[]Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (N. Yang, R. Wang, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([arxiv],[supplementary],[video],[talk],[project]) [bibtex]Oral Presentation
[]LDSO: Direct Sparse Odometry with Loop Closure (X. Gao, R. Wang, N. Demmel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv][video][code][project]) [bibtex]
2017
[]Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (R. Wang, M. Schw√∂rer and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supplementary][video][arxiv][project]) [bibtex] [pdf]
Powered by bibtexbrowser
Export as PDF, TEX or BIB

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching

info@vision.in.tum.de