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3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings.
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3D point clouds,” in Proceedings of the International Conference on Machine Learning (ICML) , 2018, pp. 40–49
2018
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B. Wu, A. Wan, X. Yue, and K. Keutzer, “SqueezeSeg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 1887–1893
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J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila, “Noise2Noise: Learning image restoration without clean data,” ser. Proceedings of Machine Learning Research, vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR, 10–15 Jul 2018, pp. 2965–2974
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2018
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K. Nakashima, H. Jung, Y. Oto, Y. Iwashita, R. Kurazume, and O. M. Mozos, “Learning geometric and photometric features from panoramic lidar scans for outdoor place categorization,” Advanced Robotics , vol. 32, no. 14, pp. 750–765, 2018
2018
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G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan, “Pointflow: 3d point cloud generation with continuous normalizing flows,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4541–4550
2019
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L. Caccia, H. van Hoof, A. Courville, and J. Pineau, “Deep generative modeling of lidar data,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 5034–5040
2019
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O. Martinez Mozos, K. Nakashima, H. Jung, Y. Iwashita, and R. Kurazume, “Fukuoka datasets for place categorization,” The International Journal of Robotics Research (IJRR) , vol. 38, no. 5, pp. 507–517, 2019
2019
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2020
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S. Manivasagam, S. Wang, K. Wong, W. Zeng, M. Sazanovich, S. Tan, B. Yang, W.-C. Ma, and R. Urtasun, “Lidarsim: Realistic lidar simulation by leveraging the real world,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 11 167–11 176
2020
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T. Kaneko and T. Harada, “Noise robust generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 8404–8414
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S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “PULSE: Self-supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 2437–2445
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