Fetching the paper…
Reading the bibliography…
Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation with structural correspondence learning,” in Proceedings of the 2006 conference on empirical methods in natural language processing . Association for Computational Linguistics, 2006, pp. 120–128
2006
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 , 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Earlier work this paper cites.
P. Germain, A. Habrard, F. Laviolette, and E. Morvant, “A pac-bayesian approach for domain adaptation with specialization to linear classifiers,” in International conference on machine learning , 2013, pp. 738–746
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3061–3070
2015
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European conference on computer vision . Springer, 2016, pp. 694–711
2016
Earlier work this paper cites.
K. Saleh, M. Hossny, A. Hossny, and S. Nahavandi, “Cyclist detection in lidar scans using faster r-cnn and synthetic depth images,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2017, pp. 1–6
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2223–2232
2017
Cited alongside, same era.
B. Li, “3d fully convolutional network for vehicle detection in point cloud,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 1513–1518
2017
Cited alongside, same era.
L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 1019–1024
2017
Cited alongside, same era.
A. Dewan, G. L. Oliveira, and W. Burgard, “Deep semantic classification for 3d lidar data,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 3544–3549
2017
Cited alongside, same era.
K. Saleh, M. Hossny, and S. Nahavandi, “Cyclist trajectory prediction using bidirectional recurrent neural networks,” in Australasian Joint Conference on Artificial Intelligence . Springer, 2018, pp. 284–295
2018
Later among the works it cites.
A. Abobakr, M. Hossny, H. Abdelkader, and S. Nahavandi, “Rgb-d fall detection via deep residual convolutional lstm networks,” in 2018 Digital Image Computing: Techniques and Applications (DICTA) , Dec 2018, pp. 1–7
2018
Later among the works it cites.
K. Saleh, M. Hossny, and S. Nahavandi, “Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning,” in 2018 Digital Image Computing: Techniques and Applications (DICTA) . IEEE, 2018, pp. 1–8
2018
Later among the works it cites.
K. Saleh, M. Hossny, and S. Nahavandi, “Effective vehicle-based kangaroo detection for collision warning systems using region-based convolutional networks,” Sensors , vol. 18, no. 6, p. 1913, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7167–7176
2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Cited alongside, same era.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv , 2018
2018
Cited alongside, same era.
K. Saleh, R. A. Zeineldin, M. Hossny, S. Nahavandi, and N. El-Fishawy, “End-to-end indoor navigation assistance for the visually impaired using monocular camera,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2018, pp. 3504–3510
2018
Cited alongside, same era.
K. Saleh, M. Attia, M. Hossny, S. Hanoun, S. Salaken, and S. Nahavandi, “Local motion planning for ground mobile robots via deep imitation learning,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2018, pp. 4077–4082
2018
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing , vol. 312, pp. 135–153, 2018
2018
Later among the works it cites.
A. Atapour-Abarghouei and T. P. Breckon, “Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2800–2810
2018
Later among the works it cites.
L. Zhang, A. Gonzalez-Garcia, J. van de Weijer, M. Danelljan, and F. S. Khan, “Synthetic data generation for end-to-end thermal infrared tracking,” IEEE Transactions on Image Processing , vol. 28, no. 4, pp. 1837–1850, 2019
2019
Closest in time.