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pro vyhledávání: '"Kreutz, Thomas"'
In this paper, we propose LiOn-XA, an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation to bridge the domain gap arising from envir
Externí odkaz:
http://arxiv.org/abs/2410.15833
The data collected from a vehicle's Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses the importance of unsupervised machine learning methods. This work pres
Externí odkaz:
http://arxiv.org/abs/2301.04988
In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS stron
Externí odkaz:
http://arxiv.org/abs/2212.14750
Autor:
Kreutz, Thomas G., Larson, Eric D., Elsido, Cristina, Martelli, Emanuele, Greig, Chris, Williams, Robert H.
Publikováno v:
In Applied Energy 1 December 2020 279
Autor:
Larson, Eric D., Kreutz, Thomas G., Greig, Chris, Williams, Robert H., Rooney, Tim, Gray, Edward, Elsido, Cristina, Martelli, Emanuele, Meerman, Johannes C.
Publikováno v:
In Applied Energy 15 February 2020 260
Publikováno v:
In Applied Energy 1 April 2019 239:1322-1342
Publikováno v:
In Chemical Engineering Research and Design August 2013 91(8):1467-1482
Publikováno v:
In Energy Procedia 2011 4:1989-1996
Publikováno v:
In Energy Procedia 2011 4:1843-1850
Publikováno v:
In Energy Procedia February 2009 1(1):4379-4386