DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (bibtex)
by R. Wang, N. Yang, J. Stueckler and D. Cremers
Reference:
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020. ([video][presentation][project page][supplementary][arxiv])
Bibtex Entry:
@inproceedings{wang2020directshape,
 author = {R. Wang and N. Yang and J. Stueckler and D. Cremers},
 title = {DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation},
 booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2020},
 keywords = {stereo, 3D reconstruction, semantic SLAM, 3D object detection, scene understanding, direct shape},
}
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DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (bibtex)
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (bibtex)
by R. Wang, N. Yang, J. Stueckler and D. Cremers
Reference:
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020. ([video][presentation][project page][supplementary][arxiv])
Bibtex Entry:
@inproceedings{wang2020directshape,
 author = {R. Wang and N. Yang and J. Stueckler and D. Cremers},
 title = {DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation},
 booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2020},
 keywords = {stereo, 3D reconstruction, semantic SLAM, 3D object detection, scene understanding, direct shape},
}
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Nan Yang

PhD student

Technical University of Munich

School of Computation, Information and Technology
Informatics 9
Boltzmannstrasse 3
85748 Garching
Germany

Fax: +49-89-289-17757
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Mail: yangn@in.tum.de

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Research Interests

My research interests lie in incorporating deep learning into classical visual odometry and SLAM.

Brief Bio

Nan Yang received his Bachelor's degree in Computer Science from Beijing University of Posts and Telecommunications and his Master's degree in Informatics from the Technical University of Munich. Since May 2018, he is a Ph.D. student and senior computer vision researcher in Artisense, a startup co-founded by Prof. Daniel Cremers.

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