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members:wangr [2018/07/28 10:09]
Rui Wang |
members:wangr [2018/08/24 15:01]
yangn |
| ==== News ==== | ==== News ==== |
| * Jul 05 2018: We have one paper accepted by ECCV'18 (oral) and two papers accepted by IROS'18. | * Jul 05 2018: We have one paper accepted by ECCV'18 (oral) and two papers accepted by IROS'18. |
| * Jun - Dec 2018: I will be interning with Prof. **Dieter Fox** in the recently founded Nvidia Robotics Research Lab in Seattle. | * Jun - Dec 2018: I will be interning with Prof. 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 [[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**. | * 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. |
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| * 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. | * 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. |
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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> |
| <html><br /><br /></html> | <html><br /></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> |
| <html><br /><br /></html> | <html><br /></html> |
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| === Visual Odometry / SLAM + Deep Learning === | === 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> |
| <html><br /><br /></html> | <html><br /></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> |
| <html><br /><br /></html> | <html><br /></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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