MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (bibtex)

by F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers

Reference:

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page])

Bibtex Entry:

@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@inproceedings{wimbauer2020monorec,
 title = {MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera},
 author = {F. Wimbauer and N. Yang and L. von Stumberg and N. Zeller and D Cremers},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2021},
 eprint = {2011.11814},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
 keywords = {monorec, dvso, d3vo, mvs, deep learning, SLAM, vslam, reconstruction},
}

by F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers

Reference:

Bibtex Entry:

@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@inproceedings{wimbauer2020monorec,
 title = {MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera},
 author = {F. Wimbauer and N. Yang and L. von Stumberg and N. Zeller and D Cremers},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2021},
 eprint = {2011.11814},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
 keywords = {monorec, dvso, d3vo, mvs, deep learning, SLAM, vslam, reconstruction},
}

Research Interests
Selected Projects

Lukas von Stumberg

PhD Student

Technical University of Munich

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

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

Find me on Google Scholar Linkedin

Research Interests

My research interests are visual and visual-inertial SLAM, robotics, and 3D reconstruction. I like to apply both traditional methods and deep learning.

Selected Projects

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization

Using our novel Gauss-Newton loss we improve the robustness of direct methods against strong illumination changes and bad initializations. This allows us to accurately relocalize between different weathers. Project Page

VI-DSO: Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

In my Master thesis on direct visual-inertial odometry we demonstrate state-of-the-art performance on the EuRoC dataset. Project Page

From Monocular SLAM to Autonomous Drone Exploration

In my Bachelor thesis I have used LSD-SLAM to explore an unknown environment with an autonomous drone.

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