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Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e.g., autonomous driving, yet it still remains notoriously labor-intensive.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2012, pp. 3354–3361
2012
Earlier work this paper cites.
K. Fukunaga, Introduction to statistical pattern recognition . Elsevier, 2013
2013
Earlier work this paper cites.
D.-H. Lee et al. , “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on challenges in representation learning, ICML , vol. 3, no. 2, 2013, p. 896
2013
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018
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 CVPR , 2018, pp. 4413–4421
2018
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.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Earlier work this paper cites.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in IEEE/CVF international conference on computer vision , 2019, pp. 9775–9784
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 529–10 538
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2446–2454
2020
Earlier work this paper cites.
Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 040–11 048
2020
Earlier work this paper cites.
S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 8, pp. 2647–2664, 2020
2020
Earlier work this paper cites.
S. Xie, J. Gu, D. Guo, C. R. Qi, L. Guibas, and O. Litany, “Pointcontrast: Unsupervised pre-training for 3d point cloud understanding,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16 . Springer, 2020, pp. 574–591
2020
Earlier work this paper cites.
X. Jia, L. Sun, M. Tomizuka, and W. Zhan, “Ide-net: Interactive driving event and pattern extraction from human data,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3065–3072, 2021
2021
Earlier work this paper cites.
T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 784–11 793
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 Conference on Computer Vision and Pattern Recognition , 2021, pp. 9939–9948
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Liang, C. Jiang, D. Feng, X. Chen, H. Xu, X. Liang, W. Zhang, Z. Li, and L. Van Gool, “Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 3293–3302
2021
Earlier work this paper cites.
S. Huang, Y. Xie, S.-C. Zhu, and Y. Zhu, “Spatio-temporal self-supervised representation learning for 3d point clouds,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 6535–6545
2021
Earlier work this paper cites.
X. Pan, Z. Xia, S. Song, L. E. Li, and G. Huang, “3d object detection with pointformer,” in IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 7463–7472
2021
Earlier work this paper cites.
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang, and H. Li, “Voxel r-cnn: Towards high performance voxel-based 3d object detection,” in AAAI conference on artificial intelligence , vol. 35, no. 2, 2021, pp. 1201–1209
2021
Cited alongside, same era.
Z. Li, F. Wang, and N. Wang, “Lidar r-cnn: An efficient and universal 3d object detector,” in IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 7546–7555
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.
X. Jia, L. Sun, H. Zhao, M. Tomizuka, and W. Zhan, “Multi-agent trajectory prediction by combining egocentric and allocentric views,” in 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, vol. 164. PMLR, 08–11 Nov 2022, pp. 1434–1443
Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-perspective view for vision-based 3d semantic occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9223–9232
2023
Closest in time.
Z. Xia, Y. Liu, X. Li, X. Zhu, Y. Ma, Y. Li, Y. Hou, and Y. Qiao, “Scpnet: Semantic scene completion on point cloud,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 642–17 651
2023
Closest in time.
X. Wang, Z. Zhu, W. Xu, Y. Zhang, Y. Wei, X. Chi, Y. Ye, D. Du, J. Lu, and X. Wang, “Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 850–17 859
2023
Closest in time.
Y. Zhang, Z. Zhu, and D. Du, “Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 9433–9443
2023
Closest in time.
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2022
Cited alongside, same era.
X. Jia, P. Wu, L. Chen, H. Li, Y. S. Liu, and J. Yan, “Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, pp. 13 860–13 875, 2022
2022
Cited alongside, same era.
P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y. Qiao, “Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,” in Advances in Neural Information Processing Systems , vol. 35, 2022, pp. 6119–6132
2022
Cited alongside, same era.
2022
Cited alongside, same era.
O. Unal, D. Dai, and L. Van Gool, “Scribble-supervised lidar semantic segmentation,” in IEEE conference on computer vision and pattern recognition , 2022, pp. 2697–2707
2022
Cited alongside, same era.
J. Yin, D. Zhou, L. Zhang, J. Fang, C.-Z. Xu, J. Shen, and W. Wang, “Proposalcontrast: Unsupervised pre-training for lidar-based 3d object detection,” in European conference on computer vision . Springer, 2022, pp. 17–33
2022
Cited alongside, same era.
A.-Q. Cao and R. de Charette, “Monoscene: Monocular 3d semantic scene completion,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3991–4001
2022
Cited alongside, same era.
H. Wang, X. Guo, Z.-H. Deng, and Y. Lu, “Rethinking minimal sufficient representation in contrastive learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 041–16 050
2022
Cited alongside, same era.
X. Jia, L. Chen, P. Wu, J. Zeng, J. Yan, H. Li, and Y. Qiao, “Towards capturing the temporal dynamics for trajectory prediction: a coarse-to-fine approach,” in 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, vol. 205, 2023, pp. 910–920
2023
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
D. D. Team, “3dtrans: An open-source codebase for exploring transferable autonomous driving perception task,” https://github.com/PJLab-ADG/3DTrans , 2023
2023
Closest in time.
H. Wang, C. Shi, S. Shi, M. Lei, S. Wang, D. He, B. Schiele, and L. Wang, “Dsvt: Dynamic sparse voxel transformer with rotated sets,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 520–13 529
2023
Closest in time.
X. Jia, S. Shi, Z. Chen, L. Jiang, W. Liao, T. He, and J. Yan, “Amp: Autoregressive motion prediction revisited with next token prediction for autonomous driving,” 2024
2024
Closest in time.
Q. Li, X. Jia, S. Wang, and J. Yan, “Think2drive: Efficient reinforcement learning by thinking in latent world model for quasi-realistic autonomous driving (in carla-v2),” in ECCV , 2024
2024
Closest in time.
X. Jia, Z. Yang, Q. Li, Z. Zhang, and J. Yan, “Bench2drive: Towards multi-ability benchmarking of closed-loop end-to-end autonomous driving,” in NeurIPS , 2024
2024
Closest in time.
2024
Closest in time.
J. Yuan, B. Zhang, X. Yan, B. Shi, T. Chen, Y. Li, and Y. Qiao, “Ad-pt: Autonomous driving pre-training with large-scale point cloud dataset,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
Z. Lin, Y. Wang, S. Qi, N. Dong, and M.-H. Yang, “Bev-mae: Bird’s eye view masked autoencoders for point cloud pre-training in autonomous driving scenarios,” in AAAI Conference on Artificial Intelligence , vol. 38, no. 4, 2024, pp. 3531–3539
2024
Closest in time.
C. Min, D. Zhao, L. Xiao, J. Zhao, X. Xu, Z. Zhu, L. Jin, J. Li, Y. Guo, J. Xing et al. , “Driveworld: 4d pre-trained scene understanding via world models for autonomous driving,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 522–15 533
2024
Closest in time.
Q. Ma, X. Tan, Y. Qu, L. Ma, Z. Zhang, and Y. Xie, “Cotr: Compact occupancy transformer for vision-based 3d occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 936–19 945
2024
Closest in time.
A. Vobecky, O. Siméoni, D. Hurych, S. Gidaris, A. Bursuc, P. Pérez, and J. Sivic, “Pop-3d: Open-vocabulary 3d occupancy prediction from images,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
P. Tang, Z. Wang, G. Wang, J. Zheng, X. Ren, B. Feng, and C. Ma, “Sparseocc: Rethinking sparse latent representation for vision-based semantic occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 035–15 044
2024
Closest in time.
S. Li, W. Yang, and Q. Liao, “Pmafusion: Projection-based multi-modal alignment for 3d semantic occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 3627–3634
2024
Closest in time.
L. Zhao, X. Xu, Z. Wang, Y. Zhang, B. Zhang, W. Zheng, D. Du, J. Zhou, and J. Lu, “Lowrankocc: Tensor decomposition and low-rank recovery for vision-based 3d semantic occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 9806–9815
2024
Closest in time.
Y. Huang, W. Zheng, B. Zhang, J. Zhou, and J. Lu, “Selfocc: Self-supervised vision-based 3d occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 946–19 956
2024
Closest in time.
X. Tian, T. Jiang, L. Yun, Y. Mao, H. Yang, Y. Wang, Y. Wang, and H. Zhao, “Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su et al. , “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” in ECCV , 2025, pp. 38–55
2025
Closest in time.