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Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation.
Pointdan: A multi-scale 3d domain adaption network for point cloud representation
Qin, C.; You, H.; Wang, L.; Kuo, C.-C. J.; and Fu, Y. 2019 · 1911
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
Earlier work this paper cites.
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
Pan, Y.; Gao, B.; Mei, J.; Geng, S.; Li, C.; and Zhao, H. 2020 · 2002
Earlier work this paper cites.
Zhao, S.; Wang, Y.; Li, B.; Wu, B.; Gao, Y.; Xu, P.; Darrell, T.; and Keutzer, K. 2020 · 2009
Earlier work this paper cites.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
Zhu, X.; Zhou, H.; Wang, T.; Hong, F.; Ma, Y.; Li, W.; Li, H.; and Lin, D. 2020 · 2011
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Tzeng, E.; Hoffman, J.; Zhang, N.; Saenko, K.; and Darrell, T. 2014 · 2014
Earlier work this paper cites.
Unsupervised domain adaptation for zero-shot learning
Kodirov, E.; Xiang, T.; Fu, Z.; and Gong, S. 2015 · 2015
Earlier work this paper cites.
Deep convolutional neural fields for depth estimation from a single image
Liu, F.; Shen, C.; and Lin, G. 2015 · 2015
Earlier work this paper cites.
3d semantic parsing of large-scale indoor spaces
Armeni, I.; Sener, O.; Zamir, A. R.; Jiang, H.; Brilakis, I.; Fischer, M.; and Savarese, S. 2016 · 2016
Earlier work this paper cites.
Virtual worlds as proxy for multi-object tracking analysis
Gaidon, A.; Wang, Q.; Cabon, Y.; and Vig, E. 2016 · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Richter, S. R.; Vineet, V.; Roth, S.; and Koltun, V. 2016 · 2016
Earlier work this paper cites.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G.; Sellart, L.; Materzynska, J.; Vazquez, D.; and Lopez, A. M. 2016 · 2016
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
Fan, H.; Su, H.; and Guibas, L. J. 2017 · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
Earlier work this paper cites.
Adversarial discriminative domain adaptation
Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017 · 2017
Earlier work this paper cites.
Learning representations and generative models for 3d point clouds
Achlioptas, P.; Diamanti, O.; Mitliagkas, I.; and Guibas, L. 2018 · 2018
Cited alongside, same era.
3d semantic segmentation with submanifold sparse convolutional networks
Graham, B.; Engelcke, M.; and Van Der Maaten, L. 2018 · 2018
Cited alongside, same era.
A network architecture for point cloud classification via automatic depth images generation
Roveri, R.; Rahmann, L.; Oztireli, C.; and Gross, M. 2018 · 2018
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K.; Watanabe, K.; Ushiku, Y.; and Harada, T. 2018 · 2018
Cited alongside, same era.
A survey on deep transfer learning
Tan, C.; Sun, F.; Kong, T.; Zhang, W.; Yang, C.; and Liu, C. 2018 · 2018
Cited alongside, same era.
Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Confidence regularized self-training
Zou, Y.; Yu, Z.; Liu, X.; Kumar, B.; and Wang, J. 2019 · 2019
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
Caesar, H.; Bankiti, V.; Lang, A. H.; Vora, S.; Liong, V. E.; Xu, Q.; Krishnan, A.; Pan, Y.; Baldan, G.; and Beijbom, O. 2020 · 2020
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Pointmixup: Augmentation for point clouds
Chen, Y.; Hu, V. T.; Gavves, E.; Mensink, T.; Mettes, P.; Yang, P.; and Snoek, C. G. 2020 · 2020
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Neural point cloud rendering via multi-plane projection
Dai, P.; Zhang, Y.; Li, Z.; Liu, S.; and Zeng, B. 2020 · 2020
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Deep learning for 3d point clouds: A survey
Guo, Y.; Wang, H.; Hu, Q.; Liu, H.; Liu, L.; and Bennamoun, M. 2020 · 2020
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RandLA-Net: Efficient semantic segmentation of large-scale point clouds
Hu, Q.; Yang, B.; Xie, L.; Rosa, S.; Guo, Y.; Wang, Z.; Trigoni, N.; and Markham, A. 2020 · 2020
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Wu, B.; Wan, A.; Yue, X.; and Keutzer, K. 2018 · 2018
Cited alongside, same era.
SemanticKITTI: A dataset for semantic scene understanding of lidar sequences
Behley, J.; Garbade, M.; Milioto, A.; Quenzel, J.; Behnke, S.; Stachniss, C.; and Gall, J. 2019 · 2019
Cited alongside, same era.
Precise synthetic image and lidar (presil) dataset for autonomous vehicle perception
Hurl, B.; Czarnecki, K.; and Waslander, S. 2019 · 2019
Cited alongside, same era.
Pu-gan: a point cloud upsampling adversarial network
Li, R.; Li, X.; Fu, C.-W.; Cohen-Or, D.; and Heng, P.-A. 2019 · 2019
Cited alongside, same era.
RangeNet++: Fast and accurate LiDAR semantic segmentation
Milioto, A.; Vizzo, I.; Behley, J.; and Stachniss, C. 2019 · 2019
Cited alongside, same era.
Domain Adaptation for Vehicle Detection from Bird’s Eye View LiDAR Point Cloud Data
Saleh, K.; Abobakr, A.; Attia, M.; Iskander, J.; Nahavandi, D.; Hossny, M.; and Nahvandi, S. 2019 · 2019
Cited alongside, same era.
Not all areas are equal: Transfer learning for semantic segmentation via hierarchical region selection
Sun, R.; Zhu, X.; Wu, C.; Huang, C.; Shi, J.; and Ma, L. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
PF-Net: Point fractal network for 3D point cloud completion
Huang, Z.; Yu, Y.; Xu, J.; Ni, F.; and Le, X. 2020 · 2020
Later among the works it cites.
Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation
Kim, T.; and Kim, C. 2020 · 2020
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Pointaugment: an auto-augmentation framework for point cloud classification
Li, R.; Li, X.; Heng, P.-A.; and Fu, C.-W. 2020 · 2020
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Searching efficient 3d architectures with sparse point-voxel convolution
Tang, H.; Liu, Z.; Zhao, S.; Lin, Y.; Lin, J.; Wang, H.; and Han, S. 2020 · 2020
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Unreal game engine
UE4. 2014 · 2021
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FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation
Xiao, A.; Yang, X.; Lu, S.; Guan, D.; and Huang, J. 2021 · 2021
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Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
Xie, C.; Wang, C.; Zhang, B.; Yang, H.; Chen, D.; and Wen, F. 2021 · 2021
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ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
Yang, J.; Shi, S.; Wang, Z.; Li, H.; and Qi, X. 2021 · 2021
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Complete & label: A domain adaptation approach to semantic segmentation of LiDAR point clouds
Yi, L.; Gong, B.; and Funkhouser, T. 2021 · 2021
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