Fetching the paper…
Reading the bibliography…
Semantic segmentation is a critical method in the field of autonomous driving.
J. Wang, K. Yang, W. Hu, and K. Wang, “An environmental perception and navigational assistance system for visually impaired persons based on semantic stixels and sound interaction,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2018, pp. 1921–1926
1926
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2015
2015
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2016, pp. 3213–3223
2016
Earlier work this paper cites.
L. Deng, M. Yang, Y. Qian, C. Wang, and B. Wang, “Cnn based semantic segmentation for urban traffic scenes using fisheye camera,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 231–236
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
K. Yang, L. M. Bergasa, E. Romera, R. Cheng, T. Chen, and K. Wang, “Unifying terrain awareness through real-time semantic segmentation,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1033–1038
2018
Earlier work this paper cites.
X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang, “The apolloscape dataset for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 954–960
2018
Earlier work this paper cites.
N. Long, K. Wang, R. Cheng, K. Yang, and J. Bai, “Fusion of millimeter wave radar and rgb-depth sensors for assisted navigation of the visually impaired,” in Millimetre Wave and Terahertz Sensors and Technology XI , vol. 10800. International Society for Optics and Photonics, 2018, p. 1080006
2018
Cited alongside, same era.
K. Narioka, H. Nishimura, T. Itamochi, and T. Inomata, “Understanding 3d semantic structure around the vehicle with monocular cameras,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 132–137
2018
Cited alongside, same era.
Y. Wu, T. Yang, J. Zhao, L. Guan, and W. Jiang, “Vh-hfcn based parking slot and lane markings segmentation on panoramic surround view,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1767–1772
2018
Cited alongside, same era.
J. Yeol Baek, I. Veronica Chelu, L. Iordache, V. Paunescu, H. Ryu, A. Ghiuta, A. Petreanu, Y. Soh, A. Leica, and B. Jeon, “Scene understanding networks for autonomous driving based on around view monitoring system,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 961–968
K. Yang, X. Hu, L. M. Bergasa, E. Romera, and K. Wang, “Pass: Panoramic annular semantic segmentation,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Deng, M. Yang, H. Li, T. Li, B. Hu, and C. Wang, “Restricted deformable convolution-based road scene semantic segmentation using surround view cameras,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Á. Sáez, L. M. Bergasa, E. Romeral, E. López, R. Barea, and R. Sanz, “Cnn-based fisheye image real-time semantic segmentation,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1039–1044
2018
Cited alongside, same era.
G. Blott, M. Takami, and C. Heipke, “Semantic segmentation of fisheye images,” in European Conference on Computer Vision . Springer, 2018, pp. 181–196
2018
Cited alongside, same era.
M. Orsic, I. Kreso, P. Bevandic, and S. Segvic, “In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 607–12 616
2019
Cited alongside, same era.
K. Yang, X. Hu, L. M. Bergasa, E. Romera, X. Huang, D. Sun, and K. Wang, “Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 446–453
2019
Cited alongside, same era.
2019
Later among the works it cites.
Á. Sáez, L. M. Bergasa, E. López-Guillén, E. Romera, M. Tradacete, C. Gómez-Huélamo, and J. del Egido, “Real-time semantic segmentation for fisheye urban driving images based on erfnet,” Sensors , vol. 19, no. 3, p. 503, 2019
2019
Later among the works it cites.
Y. Qian, M. Yang, X. Zhao, C. Wang, and B. Wang, “Oriented spatial transformer network for pedestrian detection using fish-eye camera,” IEEE Transactions on Multimedia , 2019
2019
Later among the works it cites.
A. R. Sekkat, Y. Dupuis, P. Vasseur, and P. Honeine, “The omniscape dataset,” in 2020 International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 1–6
2020
Closest in time.