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pro vyhledávání: '"Strecke, Michael"'
Physics-based understanding of object interactions from sensory observations is an essential capability in augmented reality and robotics. It enables to capture the properties of a scene for simulation and control. In this paper, we propose a novel a
Externí odkaz:
http://arxiv.org/abs/2309.15703
Autor:
Strecke, Michael, Stueckler, Joerg
Publikováno v:
2021 International Conference on 3D Vision (3DV)
Differentiable physics is a powerful tool in computer vision and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are known in advance. In
Externí odkaz:
http://arxiv.org/abs/2111.15318
Autor:
Strecke, Michael, Stueckler, Joerg
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020
Dynamic scene understanding is an essential capability in robotics and VR/AR. In this paper we propose Co-Section, an optimization-based approach to 3D dynamic scene reconstruction, which infers hidden shape information from intersection constraints.
Externí odkaz:
http://arxiv.org/abs/2004.04630
Autor:
Strecke, Michael, Stückler, Jörg
Publikováno v:
IEEE/CVF International Conference on Computer Vision (ICCV) 2019
The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applica
Externí odkaz:
http://arxiv.org/abs/1904.11781
Autor:
Maximilian Diebold, Marcel Gutsche, Anna Alperovich, Ole Johannsen, Shuo Zhang, Jaesik Park, Marco Carli, Michele Brizzi, Hae-Gon Jeon, Yu-Wing Tai, Sven Wanner, Bastian Goldluecke, Jinsun Park, Yunsu Bok, Zhang Xiong, Hao Sheng, Jingyi Yu, Qing Wang, Lipeng Si, Katrin Honauer, In So Kweon, Antonin Sulc, Gyeongmin Choe, Michael Strecke, Hendrik Schilling, Hao Zhu, Federica Battisti, Ting-Chun Wang
Publikováno v:
CVPR Workshops
This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ffa868510b01306b9d4840d36d0d24d
http://hdl.handle.net/11577/3363391
http://hdl.handle.net/11577/3363391