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pro vyhledávání: '"Schoeler, Markus"'
Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. In this wor
Externí odkaz:
http://arxiv.org/abs/2406.10600
Publikováno v:
Journal of Physics: Conference Series, Volume 1924
For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for environment perception based on radar scans build up on deep neural n
Externí odkaz:
http://arxiv.org/abs/2305.12775
Autor:
Papon, Jeremie, Schoeler, Markus
In this work we address the problem of indoor scene understanding from RGB-D images. Specifically, we propose to find instances of common furniture classes, their spatial extent, and their pose with respect to generalized class models. To accomplish
Externí odkaz:
http://arxiv.org/abs/1508.00835
Akademický článek
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Publikováno v:
2016 IEEE International Conference on Robotics & Automation (ICRA); 2016, p2471-2478, 8p
Publikováno v:
2015 IEEE Winter Conference on Applications of Computer Vision; 2015, p805-812, 8p
Publikováno v:
2015 IEEE Winter Conference on Applications of Computer Vision; 2015, p124-131, 8p
Publikováno v:
2015 7th International Conference on Games & Virtual Worlds for Serious Applications (VS-Games); 2015, p5207-5215, 9p
Autor:
Schoeler, Markus, Stein, Simon Christoph, Papon, Jeremie, Abramov, Alexey, Worgotter, Florentin
Publikováno v:
2015 IEEE International Conference on Rehabilitation Robotics (ICORR); 2015, p94-103, 10p