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The search for refining 3D LiDAR data has attracted growing interest motivated by recent techniques such as supervised learning or generative model-based methods.
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A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet++: Fast and accurate LiDAR semantic segmentation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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T. Shan, J. Wang, F. Chen, P. Szenher, and B. Englot, “Simulation-based lidar super-resolution for ground vehicles,” Robotics and Autonomous Systems (RAS)
2020
Cited alongside, same era.
K. Nakashima and R. Kurazume, “Learning to drop points for LiDAR scan synthesis,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2021
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Y. Chen, S. Liu, and X. Wang, “Learning continuous image representation with local implicit image function,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2021
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V. Zyrianov, X. Zhu, and S. Wang, “Learning to generate realistic lidar point clouds,” in Proceedings of the European Conference on Computer Vision (ECCV)
2022
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A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “RePaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Y. Kwon, M. Sung, and S.-E. Yoon, “Implicit LiDAR network: Lidar super-resolution via interpolation weight prediction,” in Proceedings of the International Conference on Robotics and Automation (ICRA)
2022
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C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al
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A. Xiao, J. Huang, D. Guan, F. Zhan, and S. Lu, “Transfer learning from synthetic to real LiDAR point cloud for semantic segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence
2022
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C. Saharia, W. Chan, H. Chang, C. Lee, J. Ho, T. Salimans, D. Fleet, and M. Norouzi, “Palette: Image-to-image diffusion models,” in Proceedings of the International Conference and Exhibition on Computer Graphics and Interactive Techniques (SIGGRAPH)
2022
Cited alongside, same era.
Y. Liao, J. Xie, and A. Geiger, “KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2022
Cited alongside, same era.
K. Nakashima, Y. Iwashita, and R. Kurazume, “Generative range imaging for learning scene priors of 3D LiDAR data,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
2023
Later among the works it cites.
K. Nakashima and R. Kurazume, “Lidar data synthesis with denoising diffusion probabilistic models,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
2024
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