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Range-view based LiDAR segmentation methods are attractive for practical applications due to their direct inheritance from efficient 2D CNN architectures.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
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Training deeper convolutional networks with deep supervision
L. Wang, C.-Y. Lee, Z. Tu, and S. Lazebnik · 2015
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz · 2018
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SECOND: sparsely embedded convolutional detection
Y. Yan, Y. Mao, and B. Li · 2018
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The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks
M. Berman, A. Rannen Triki, and M. B. Blaschko · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Semantickitti: A dataset for semantic scene understanding of lidar sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
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Rangenet++: Fast and accurate lidar semantic segmentation
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss · 2019
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4d spatio-temporal convnets: Minkowski convolutional neural networks
C. Choy, J. Gwak, and S. Savarese · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo · 2019
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Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation
Z. Tian, T. He, C. Shen, and Y. Yan · 2019
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Not using the car to see the sidewalk–quantifying and controlling the effects of context in classification and segmentation
R. Shetty, B. Schiele, and M. Fritz · 2019
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Boundary loss for remote sensing imagery semantic segmentation
A. Bokhovkin and E. Burnaev · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Kpconv: Flexible and deformable convolution for point clouds
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas · 2019
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Pointpillars: Fast encoders for object detection from point clouds
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom · 2019
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Panoptic segmentation
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár · 2019
Mopt: Multi-object panoptic tracking
J. V. Hurtado, R. Mohan, and A. Valada · 2020
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Per-pixel classification is not all you need for semantic segmentation
B. Cheng, A. Schwing, and A. Kirillov · 2021
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K-net: Towards unified image segmentation
W. Zhang, J. Pang, K. Chen, and C. C. Loy · 2021
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Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation
J. Xu, R. Zhang, J. Dou, Y. Zhu, J. Sun, and S. Pu · 2021
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Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin · 2021
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Fidnet: Lidar point cloud semantic segmentation with fully interpolation decoding
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Seamless scene segmentation
L. Porzi, S. R. Bulo, A. Colovic, and P. Kontschieder · 2019
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Deep learning for 3d point clouds: A survey
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun · 2020
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Deep learning for lidar point clouds in autonomous driving: A review
Y. Li, L. Ma, Z. Zhong, F. Liu, M. A. Chapman, D. Cao, and J. Li · 2020
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Randla-net: Efficient semantic segmentation of large-scale point clouds
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham · 2020
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Searching efficient 3d architectures with sparse point-voxel convolution
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han · 2020
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Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation
C. Xu, B. Wu, Z. Wang, W. Zhan, P. Vajda, K. Keutzer, and M. Tomizuka · 2020
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Y. Zhao, L. Bai, and X. Huang · 2021
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Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions
R. Razani, R. Cheng, E. Taghavi, and L. Bingbing · 2021
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Pointcutmix: Regularization strategy for point cloud classification
J. Zhang, L. Chen, B. Ouyang, B. Liu, J. Zhu, Y. Chen, Y. Meng, and D. Wu · 2021
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Mix3d: Out-of-context data augmentation for 3d scenes
A. Nekrasov, J. Schult, O. Litany, B. Leibe, and F. Engelmann · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
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Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion
X. Yan, J. Gao, J. Li, R. Zhang, Z. Li, R. Huang, and S. Cui · 2021
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Panoster: End-to-end panoptic segmentation of lidar point clouds
S. Gasperini, M.-A. N. Mahani, A. Marcos-Ramiro, N. Navab, and F. Tombari · 2021
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Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation
Z. Zhou, Y. Zhang, and H. Foroosh · 2021
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Lidar-based panoptic segmentation via dynamic shifting network
F. Hong, H. Zhou, X. Zhu, H. Li, and Z. Liu · 2021
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Efficientlps: Efficient lidar panoptic segmentation
K. Sirohi, R. Mohan, D. Büscher, W. Burgard, and A. Valada · 2021
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Masked-attention mask transformer for universal image segmentation
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar · 2022
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Panoptic segformer: Delving deeper into panoptic segmentation with transformers
Z. Li, W. Wang, E. Xie, Z. Yu, A. Anandkumar, J. M. Alvarez, P. Luo, and T. Lu · 2022
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Cenet: Toward concise and efficient lidar semantic segmentation for autonomous driving
G. X. Huixian Cheng, Xianfeng Han · 2022
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