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We propose a novel single-step training strategy that allows convolutional encoder-decoder networks that use skip connections, to complete partially observed data by means of hallucination.
1907
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D. Stutz and A. Geiger, “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1955–1964
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A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
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I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Networks,” in Advances In Neural Information Processing Systems , 2014, pp. 2672–2680
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2014
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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, pp. 234–241
2015
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G. Mattyus, S. Wang, S. Fidler, and R. Urtasun, “Enhancing Road Maps by Parsing Aerial Images Around the World,” in 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1689–1697
2015
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2015
Cited alongside, same era.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context Encoders: Feature Learning by Inpainting,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 2536–2544
2016
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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) , 2016, pp. 3213–3223
2016
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S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and locally consistent image completion,” ACM Transactions on Graphics , vol. 36, no. 4, pp. 1–14, 2017
2017
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A. Paszke, G. Chanan, Z. Lin, S. Gross, E. Yang, L. Antiga, and Z. Devito, “Automatic differentiation in PyTorch,” in Advances in Neural Information Processing Systems Workshop , 2017
2017
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S. Schulter, M. Zhai, N. Jacobs, and M. Chandraker, “Learning to Look around Objects for Top-View Representations of Outdoor Scenes,” in European Conference on Computer Vision , 2018, pp. 787–802
2018
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M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet, “Are GANs Created Equal? A Large-Scale Study,” in Advances in neural information processing systems , 2018, pp. 698–707
2018
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2018
Later among the works it cites.
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Y. Li, S. Liu, J. Yang, and M.-H. Yang, “Generative Face Completion,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 5892–5900
2017
Cited alongside, same era.
R. A. Yeh, C. Chen, T. Y. Lim, A. G. Schwing, M. Hasegawa-Johnson, and M. N. Do, “Semantic Image Inpainting with Deep Generative Models,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 6882–6890
2017
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” in 2017 IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2242–2251
2017
Cited alongside, same era.
T. Suleymanov, P. Amayo, and P. Newman, “Inferring Road Boundaries Through and Despite Traffic,” in IEEE International Conference on Intelligent Transportation Systems (ITSC) , 2018, pp. 409–416
2018
Later among the works it cites.
2018
Later among the works it cites.
C. Lu, M. J. G. van de Molengraft, and G. Dubbelman, “Monocular Semantic Occupancy Grid Mapping With Convolutional Variational Encoder–Decoder Networks,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 445–452, 2019
2019
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