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LiDAR segmentation has become a crucial component of advanced autonomous driving systems.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 2881–2890
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
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 IEEE International Conference on Robotics and Automation , 2018, pp. 1887–1893
2018
Earlier work this paper cites.
M. Berman, A. R. Triki, and M. B. Blaschko, “The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 4413–4421
2018
Earlier work this paper cites.
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in IEEE/CVF International Conference on Computer Vision , 2019, pp. 6411–6420
2019
Earlier work this paper cites.
C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3075–3084
2019
Earlier work this paper cites.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in IEEE/CVF International Conference on Computer Vision , 2019, pp. 9297–9307
2019
Earlier work this paper cites.
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “Rangenet++: Fast and accurate lidar semantic segmentation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2019, pp. 4213–4220
2019
Earlier work this paper cites.
B. Wu, X. Zhou, S. Zhao, X. Yue, and K. Keutzer, “Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud,” in IEEE International Conference on Robotics and Automation , 2019, pp. 4376–4382
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
L. N. Smith and N. Topin, “Super-convergence: Very fast training of neural networks using large learning rates,” in Artificial intelligence and machine learning for multi-domain operations applications , vol. 11006, 2019, pp. 369–386
2019
Earlier work this paper cites.
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 12, pp. 4338–4364, 2020
2020
Earlier work this paper cites.
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, “Randla-net: Efficient semantic segmentation of large-scale point clouds,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 108–11 117
2020
Earlier work this paper cites.
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in European Conference on Computer Vision , 2020, pp. 685–702
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Zhang, Z. Zhou, P. David, X. Yue, Z. Xi, B. Gong, and H. Foroosh, “Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9601–9610
2020
Earlier work this paper cites.
E. E. Aksoy, S. Baci, and S. Cavdar, “Salsanet: Fast road and vehicle segmentation in lidar point clouds for autonomous driving,” in IEEE Intelligent Vehicles Symposium , 2020, pp. 926–932
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. Xu, B. Wu, Z. Wang, W. Zhan, P. Vajda, K. Keutzer, and M. Tomizuka, “Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation,” in European Conference on Computer Vision , 2020, pp. 1–19
2020
Earlier work this paper cites.
Y. Chen, V. T. Hu, E. Gavves, T. Mensink, P. Mettes, P. Yang, and C. G. Snoek, “Pointmixup: Augmentation for point clouds,” in European Conference on Computer Vision , 2020, pp. 330–345
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Cortinhal, G. Tzelepis, and E. Erdal Aksoy, “Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds,” in Advances in Visual Computing , 2020, pp. 207–222
2020
Earlier work this paper cites.
Y. Pan, B. Gao, J. Mei, S. Geng, C. Li, and H. Zhao, “Semanticposs: A point cloud dataset with large quantity of dynamic instances,” in IEEE Intelligent Vehicles Symposium , 2020, pp. 687–693
2020
Earlier work this paper cites.
M. Contributors, “MMDetection3D: OpenMMLab next-generation platform for general 3D object detection,” https://github.com/open-mmlab/mmdetection3d , 2020
2020
Earlier work this paper cites.
I. Alonso, L. Riazuelo, L. Montesano, and A. C. Murillo, “3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5432–5439, 2020
2020
Earlier work this paper cites.
F. Zhang, J. Fang, B. Wah, and P. Torr, “Deep fusionnet for point cloud semantic segmentation,” in European Conference on Computer Vision , 2020, pp. 644–663
2020
Earlier work this paper cites.
H. Shuai, X. Xu, and Q. Liu, “Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation,” IEEE Transactions on Image Processing , vol. 30, pp. 4973–4984, 2021
2021
Earlier work this paper cites.
X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin, “Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9939–9948
2021
Earlier work this paper cites.
J. Xu, R. Zhang, J. Dou, Y. Zhu, J. Sun, and S. Pu, “Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 024–16 033
2021
Earlier work this paper cites.
Y. A. Alnaggar, M. Afifi, K. Amer, and M. ElHelw, “Multi projection fusion for real-time semantic segmentation of 3d lidar point clouds,” in EEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 1800–1809
2021
Earlier work this paper cites.
Q. Chen, S. Vora, and O. Beijbom, “Polarstream: Streaming object detection and segmentation with polar pillars,” in Advances in Neural Information Processing Systems , vol. 34, 2021, pp. 26 871–26 883
2021
Cited alongside, same era.
Z. Zhou, Y. Zhang, and H. Foroosh, “Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 13 194–13 203
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” in Advances in Neural Information Processing Systems , vol. 34, 2021, pp. 12 077–12 090
2021
Cited alongside, same era.
A. Nekrasov, J. Schult, O. Litany, B. Leibe, and F. Engelmann, “Mix3d: Out-of-context data augmentation for 3d scenes,” in IEEE International Conference on 3D Vision , 2021, pp. 116–125
2021
Cited alongside, same era.
M. Ye, R. Wan, S. Xu, T. Cao, and Q. Chen, “Efficient point cloud segmentation with geometry-aware sparse networks,” in European Conference on Computer Vision , 2022, pp. 196–212
2022
Later among the works it cites.
T. Zhang, M. Ma, F. Yan, H. Li, and Y. Chen, “Pids: Joint point interaction-dimension search for 3d point cloud,” in IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 1298–1307
2023
Closest in time.
G. Puy, A. Boulch, and R. Marlet, “Using a waffle iron for automotive point cloud semantic segmentation,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 3379–3389
2023
Closest in time.
A. Boulch, C. Sautier, B. Michele, G. Puy, and R. Marlet, “Also: Automotive lidar self-supervision by occupancy estimation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 455–13 465
2023
Closest in time.
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Y. Zhao, L. Bai, and X. Huang, “Fidnet: Lidar point cloud semantic segmentation with fully interpolation decoding,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2021, pp. 4453–4458
2021
Cited alongside, same era.
Q. Hu, B. Yang, S. Khalid, W. Xiao, N. Trigoni, and A. Markham, “Towards semantic segmentation of urban-scale 3d point clouds: A dataset, benchmarks and challenges,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4977–4987
2021
Cited alongside, same era.
R. Razani, R. Cheng, E. Taghavi, and L. Bingbing, “Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions,” in IEEE International Conference on Robotics and Automation , 2021, pp. 9550–9556
2021
Cited alongside, same era.
M. Gerdzhev, R. Razani, E. Taghavi, and L. Bingbing, “Tornado-net: multiview total variation semantic segmentation with diamond inception module,” in IEEE International Conference on Robotics and Automation , 2021, pp. 9543–9549
2021
Cited alongside, same era.
X. Yan, J. Gao, J. Li, R. Zhang, Z. Li, R. Huang, and S. Cui, “Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion,” in AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3101–3109
2021
Cited alongside, same era.
R. Cheng, R. Razani, E. Taghavi, E. Li, and B. Liu, “Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 547–12 556
2021
Cited alongside, same era.
Z. Zhuang, R. Li, K. Jia, Q. Wang, Y. Li, and M. Tan, “Perception-aware multi-sensor fusion for 3d lidar semantic segmentation,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 280–16 290
2021
Cited alongside, same era.
K. Genova, X. Yin, A. Kundu, C. Pantofaru, F. Cole, A. Sud, B. Brewington, B. Shucker, and T. Funkhouser, “Learning 3d semantic segmentation with only 2d image supervision,” in International Conference on 3D Vision , 2021, pp. 361–372
2021
Cited alongside, same era.
Y. Liu, R. Chen, X. Li, L. Kong, Y. Yang, Z. Xia, Y. Bai, X. Zhu, Y. Ma, Y. Li et al. , “Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 662–21 673
2023
Closest in time.
R. Chen, Y. Liu, L. Kong, X. Zhu, Y. Ma, Y. Li, Y. Hou, Y. Qiao, and W. Wang, “Clip2scene: Towards label-efficient 3d scene understanding by clip,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7020–7030
2023
Closest in time.
R. Chen, Y. Liu, L. Kong, N. Chen, X. Zhu, Y. Ma, T. Liu, and W. Wang, “Towards label-free scene understanding by vision foundation models,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 75 896–75 910
2023
Closest in time.
R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song et al. , “V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 712–13 722
2023
Closest in time.
H. Yu, W. Yang, H. Ruan, Z. Yang, Y. Tang, X. Gao, X. Hao, Y. Shi, Y. Pan, N. Sun et al. , “V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5486–5495
2023
Closest in time.
A. Ando, S. Gidaris, A. Bursuc, G. Puy, A. Boulch, and R. Marlet, “Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5240–5250
2023
Closest in time.
L. Kong, Y. Liu, R. Chen, Y. Ma, X. Zhu, Y. Li, Y. Hou, Y. Qiao, and Z. Liu, “Rethinking range view representation for lidar segmentation,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 228–240
2023
Closest in time.
L. Kong, N. Quader, and V. E. Liong, “Conda: Unsupervised domain adaptation for lidar segmentation via regularized domain concatenation,” in IEEE International Conference on Robotics and Automation , 2023, pp. 9338–9345
2023
Closest in time.
L. Kong, J. Ren, L. Pan, and Z. Liu, “Lasermix for semi-supervised lidar semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 705–21 715
2023
Closest in time.
Y. Liu, L. Kong, J. Cen, R. Chen, W. Zhang, L. Pan, K. Chen, and Z. Liu, “Segment any point cloud sequences by distilling vision foundation models,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 37 193–37 229
2023
Closest in time.
X. Lai, Y. Chen, F. Lu, J. Liu, and J. Jia, “Spherical transformer for lidar-based 3d recognition,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 545–17 555
2023
Closest in time.
L. Li, H. P. Shum, and T. P. Breckon, “Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9361–9371
2023
Closest in time.
L. Reichardt, N. Ebert, and O. Wasenmüller, “360deg from a single camera: A few-shot approach for lidar segmentation,” in IEEE/CVF International Conference on Computer Vision Workshops , 2023, pp. 1075–1083
2023
Closest in time.
L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 994–20 006
2023
Closest in time.
L. Kong, S. Xie, H. Hu, L. X. Ng, B. Cottereau, and W. T. Ooi, “Robodepth: Robust out-of-distribution depth estimation under corruptions,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 21 298–21 342
2023
Closest in time.
S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser et al. , “Openscene: 3d scene understanding with open vocabularies,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 815–824
2023
Closest in time.
J. Park, C. Kim, S. Kim, and K. Jo, “Pcscnet: Fast 3d semantic segmentation of lidar point cloud for autonomous car using point convolution and sparse convolution network,” Expert Systems with Applications , vol. 212, p. 118815, 2023
2023
Closest in time.
D. Ye, Z. Zhou, W. Chen, Y. Xie, Y. Wang, P. Wang, and H. Foroosh, “Lidarmultinet: Towards a unified multi-task network for lidar perception,” in AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 3231–3240
2023
Closest in time.
D. Kong, X. Li, Q. Xu, Y. Hu, and P. Ni, “Sc_lpr: Semantically consistent lidar place recognition based on chained cascade network in long-term dynamic environments,” IEEE Transactions on Image Processing , vol. 33, pp. 2145–2157, 2024
2024
Closest in time.
Y. Li, L. Kong, H. Hu, X. Xu, and X. Huang, “Is your lidar placement optimized for 3d scene understanding?” in Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
T. Sun, Z. Zhang, X. Tan, Y. Qu, and Y. Xie, “Image understands point cloud: Weakly supervised 3d semantic segmentation via association learning,” IEEE Transactions on Image Processing , vol. 33, pp. 1838–1852, 2024
2024
Closest in time.
X. Wu, L. Jiang, P.-S. Wang, Z. Liu, X. Liu, Y. Qiao, W. Ouyang, T. He, and H. Zhao, “Point transformer v3: Simpler faster stronger,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4840–4851
2024
Closest in time.
F. Hong, L. Kong, H. Zhou, X. Zhu, H. Li, and Z. Liu, “Unified 3d and 4d panoptic segmentation via dynamic shifting networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.
2024
Closest in time.
Y. Liu, L. Kong, X. Wu, R. Chen, X. Li, L. Pan, Z. Liu, and Y. Ma, “Multi-space alignments towards universal lidar segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 14 648–14 661
2024
Closest in time.
X. Wu, Y. Hou, X. Huang, B. Lin, T. He, X. Zhu, Y. Ma, B. Wu, H. Liu, D. Cai et al. , “Taseg: Temporal aggregation network for lidar semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 311–15 320
2024
Closest in time.
Q. Huang, X. Dong, D. Chen, H. Zhou, W. Zhang, K. Zhang, G. Hua, Y. Cheng, and N. Yu, “Pointcat: Contrastive adversarial training for robust point cloud recognition,” IEEE Transactions on Image Processing , vol. 33, pp. 2183–2196, 2024
2024
Closest in time.
X. Wu, Z. Tian, X. Wen, B. Peng, X. Liu, K. Yu, and H. Zhao, “Towards large-scale 3d representation learning with multi-dataset point prompt training,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 551–19 562
2024
Closest in time.
L. Kong, X. Xu, J. Cen, W. Zhang, L. Pan, K. Chen, and Z. Liu, “Calib3d: Calibrating model preferences for reliable 3d scene understanding,” in IEEE/CVF Winter Conference on Applications of Computer Vision , 2025
2025
Closest in time.