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members:wangr [2020/02/25 22:25] Rui Wang |
members:wangr [2020/02/25 22:29] Rui Wang |
=== Semantic VO / SLAM === | === Semantic VO / SLAM === |
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* ** 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 [[https://vision.in.tum.de/research/vslam/direct-shape|Project Page]]. | * ** DirectShape ** 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 [[https://vision.in.tum.de/research/vslam/direct-shape|Project Page]]. |
<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/QqP6zdx5OKw" frameborder="0" allowfullscreen></iframe></center></html> | <html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/QqP6zdx5OKw" frameborder="0" allowfullscreen></iframe></center></html> |
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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. ([[https://vision.in.tum.de/research/vslam/dvso|Project Page]]) | * ** DVSO: Deep Virtual Stereo Odometry ** 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. ([[https://vision.in.tum.de/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> |