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pro vyhledávání: '"Hasecke, Frederik"'
This technical report outlines our method for generating a synthetic dataset for semantic segmentation using a latent diffusion model. Our approach eliminates the need for additional models specifically trained on segmentation data and is part of our
Externí odkaz:
http://arxiv.org/abs/2406.17541
Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching th
Externí odkaz:
http://arxiv.org/abs/2212.09517
Publikováno v:
33rd IEEE Intelligent Vehicles Symposium, Aachen, Germany, June 5th - June 9th 2022
To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data. In this paper we present novel point cloud augmentation methods to ar
Externí odkaz:
http://arxiv.org/abs/2206.09664
Publikováno v:
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 2020, pp. 1-6
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements. Nevertheless, many of these are not deployable to embedded vehicle syst
Externí odkaz:
http://arxiv.org/abs/2006.10011
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products. With an ever growing number of different driver assistance systems, they have been introduced to automotive
Externí odkaz:
http://arxiv.org/abs/2003.00575
Publikováno v:
Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods.
Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching th