Uncertainty-aware LiDAR Panoptic Segmentation
Author:
Affiliation:
1. University of Freiburg,Department of Computer Science,Germany
2. Technical University,Department of Engineering,Nürnberg,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160355.pdf?arnumber=10160355
Reference31 articles.
1. Dropout as a bayesian approximation: Representing model uncertainty in deep learning;gal;Int Conference on Machine Learning,0
2. Evidential deep learning to quantify classification uncertainty;sensoy;Advances in neural information processing systems,2018
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4. Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding;kendall;ArXiv Preprint,2015
5. A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
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