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members:wangr [2018/07/28 10:12] Rui Wang |
members:wangr [2019/04/04 18:55] Rui Wang |
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==== News ==== | ==== News ==== |
* Jul 05 2018: We have one paper accepted by ECCV'18 (oral) and two papers accepted by IROS'18. | * [10.2018] The code for LDSO (Direct Sparse Odometry with Loop Closure) has been released! Please visit the [[https://vision.in.tum.de/research/vslam/ldso|project page]] for details. |
* Jun - Dec 2018: I will be interning with Prof. **Dieter Fox** in the recently founded Nvidia Robotics Research Lab in Seattle. | * [07.2018] We have one paper accepted by ECCV'18 (oral) and two papers accepted by IROS'18. |
* May 22 2018: We have released our code for online photometric calibration! Please find the link on the [[https://vision.in.tum.de/research/vslam/photometric-calibration|Project Page]]. The paper was recently nominated by ICRA'18 for the **Best Vision Paper Award**. | * [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 [[https://vision.in.tum.de/research/vslam/photometric-calibration|project page]]. The paper was recently nominated by ICRA'18 for the Best Vision Paper Award. |
* Mar 2018: I join [[https://www.artisense.ai/|Artisense]], a startup co-founded by Prof. **Daniel Cremers**, as a PhD student and senior computer vision & AI researcher. | * [03.2018] I join [[https://www.artisense.ai/|Artisense]], a startup co-founded by Prof. Daniel Cremers, as a PhD Student and Senior Computer Vision & AI Researcher. |
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* ** 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. ([[https://vision.in.tum.de/research/vslam/stereo-dso|Project Page]]) | * ** 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. ([[https://vision.in.tum.de/research/vslam/stereo-dso|Project Page]]) |
<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/A53vJO8eygw" frameborder="0" allowfullscreen></iframe></center></html> | <html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/A53vJO8eygw" frameborder="0" allowfullscreen></iframe></center></html> |
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* ** 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. ([[https://vision.in.tum.de/research/vslam/stereo-dso|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. ([[https://vision.in.tum.de/research/vslam/stereo-dso|Project Page]]) |
<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/BxTLhubqEKg" frameborder="0" allowfullscreen></iframe></center></html> | <html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/BxTLhubqEKg" frameborder="0" allowfullscreen></iframe></center></html> |
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| * ** 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 [[https://vision.in.tum.de/research/vslam/ldso|Project Page]]. |
| <html><center><iframe width="640" height="360" |
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=== Deep Learning Boosted VO / SLAM === | === Deep Learning Boosted VO / SLAM === |
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* ** 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 coming soon) | * ** 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. ([[:research:vslam:dvso|Project Page]]) |
<html><center><iframe width="640" height="360" | <html><center><iframe width="640" height="360" |
src="https://www.youtube.com/embed/sLZOeC9z_tw" frameborder="0" allowfullscreen></iframe> | src="https://www.youtube.com/embed/sLZOeC9z_tw" frameborder="0" allowfullscreen></iframe> |
</center></html> | </center></html> |
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=== Camera Calibration === | === 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". ([[https://vision.in.tum.de/research/vslam/photometric-calibration|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". ([[https://vision.in.tum.de/research/vslam/photometric-calibration|Project Page]]) |
<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/nQHMG0c6Iew" frameborder="0" allowfullscreen></iframe></center></html> | <html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/nQHMG0c6Iew" frameborder="0" allowfullscreen></iframe></center></html> |
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==== Master Theses / IDP / Guided Research ==== | ==== Master Theses / IDP / Guided Research ==== |
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* Summer Semester 2016 [[teaching:ss2016:mvg2016|Computer Vision II: Multiple View Geometry (IN2228)]] | * Summer Semester 2016 [[teaching:ss2016:mvg2016|Computer Vision II: Multiple View Geometry (IN2228)]] |
* Summer Semester 2017 [[teaching:ss2017:mvg2017|Computer Vision II: Multiple View Geometry (IN2228)]] <html><span style="color:red;">Best Elective Lecture Award</span></html> | * Summer Semester 2017 [[teaching:ss2017:mvg2017|Computer Vision II: Multiple View Geometry (IN2228)]] <html><span style="color:black;">Best Elective Lecture Award</span></html> |
* Winter Semester 2017/18 [[teaching:ws2017:r3dv|Robotic 3D Vision]] | * Winter Semester 2017/18 [[teaching:ws2017:r3dv|Robotic 3D Vision]] |
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| ==== Service ==== |
| Reviewer: CVPR, ICCV, ICRA, IROS, RA-L |
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==== Publications ==== | ==== Publications ==== |