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LiDAR-based sparse 3D object detection plays a crucial role in autonomous driving applications due to its computational efficiency advantages.
O. Team et al. , “Openpcdet: An open-source toolbox for 3d object detection from point clouds,” 2020
2001
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . IEEE, 2012, pp. 3354–3361. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6248074
2012
Earlier work this paper cites.
2018
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. [Online]. Available: http://dblp.uni-trier.de/db/journals/sensors/sensors18.html#YanML18
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection.” in CVPR . IEEE Computer Society, 2018, pp. 4490–4499. [Online]. Available: http://dblp.uni-trier.de/db/conf/cvpr/cvpr2018.html#ZhouT18
2018
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud.” in CVPR . Computer Vision Foundation / IEEE, 2019, pp. 770–779. [Online]. Available: http://dblp.uni-trier.de/db/conf/cvpr/cvpr2019.html#ShiWL19
2019
Earlier work this paper cites.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9775–9784
2019
Earlier work this paper cites.
Q. Chen, L. Sun, Z. Wang, K. Jia, and A. Yuille, “Object as hotspots: An anchor-free 3d object detection approach via firing of hotspots,” Cornell University - arXiv,Cornell University - arXiv , Dec 2019
2019
Earlier work this paper cites.
W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 1711–1719
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 Proceedings of the 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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2446–2454
2020
Earlier work this paper cites.
H. Kuang, B. Wang, J. An, M. Zhang, and Z. Zhang, “Voxel-FPN: Multi-scale voxel feature aggregation for 3D object detection from LIDAR point clouds,” Sensors , vol. 20, no. 3, p. 704, 2020
2020
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 529–10 538
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. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4604–4612
2020
Earlier work this paper cites.
Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 040–11 048
2020
Earlier work this paper cites.
J. Wang, S. Lan, M. Gao, and L. S. Davis, “Infofocus: 3d object detection for autonomous driving with dynamic information modeling,” in European Conference on Computer Vision . Springer, 2020, pp. 405–420
2020
Earlier work this paper cites.
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou, and X. Bai, “Tanet: Robust 3d object detection from point clouds with triple attention,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 11 677–11 684
2020
Earlier work this paper cites.
B. Xu, X. Zhang, L. Wang, X. Hu, Z. Li, S. Pan, J. Li, and Y. Deng, “Rpfa-net: A 4d radar pillar feature attention network for 3d object detection,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 3061–3066
2021
Earlier work this paper cites.
T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 11 784–11 793
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 Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 2, 2021, pp. 1201–1209
2021
Earlier work this paper cites.
P. Gao, M. Zheng, X. Wang, J. Dai, and H. Li, “Fast convergence of detr with spatially modulated co-attention,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , Oct 2021. [Online]. Available: http://dx.doi.org/10.1109/iccv48922.2021.00360
2021
Earlier work this paper cites.
J. Deng, W. Zhou, Y. Zhang, and H. Li, “From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 12, pp. 4722–4734, 2021
2021
Earlier work this paper cites.
Z. Li, Y. Yao, Z. Quan, W. Yang, and J. Xie, “Sienet: Spatial information enhancement network for 3d object detection from point cloud.” Cornell University - arXiv,Cornell University - arXiv , Mar 2021
2021
Earlier work this paper cites.
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X.-S. Hua, and M.-J. Zhao, “Improving 3d object detection with channel-wise transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2743–2752
2021
Cited alongside, same era.
Y. Zhang, D. Huang, and Y. Wang, “Pc-rgnn: Point cloud completion and graph neural network for 3d object detection.” Cornell University - arXiv,Cornell University - arXiv , May 2021
2021
Cited alongside, same era.
X. Zhang, L. Wang, G. Zhang, T. Lan, H. Zhang, L. Zhao, J. Li, L. Zhu, and H. Liu, “Ri-fusion: 3d object detection using enhanced point features with range-image fusion for autonomous driving,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–13, 2022
2022
Cited alongside, same era.
L. Wang, X. Zhang, B. Xv, J. Zhang, R. Fu, X. Wang, L. Zhu, H. Ren, P. Lu, J. Li et al. , “Interfusion: Interaction-based 4d radar and lidar fusion for 3d object detection,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 12 247–12 253
L. Yang, K. Yu, T. Tang, J. Li, K. Yuan, L. Wang, X. Zhang, and P. Chen, “Bevheight: A robust framework for vision-based roadside 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 611–21 620
2023
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2023
Later among the works it cites.
L. Yang, X. Zhang, J. Li, L. Wang, M. Zhu, C. Zhang, and H. Liu, “Mix-teaching: A simple, unified and effective semi-supervised learning framework for monocular 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
L. Wang, X. Zhang, Z. Song, J. Bi, G. Zhang, H. Wei, L. Tang, L. Yang, J. Li, C. Jia et al. , “Multi-modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy,” IEEE Transactions on Intelligent Vehicles , 2023
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2022
Cited alongside, same era.
L. Wang, X. Zhang, J. Li, B. Xv, R. Fu, H. Chen, L. Yang, D. Jin, and L. Zhao, “Multi-modal and multi-scale fusion 3d object detection of 4d radar and lidar for autonomous driving,” IEEE Transactions on Vehicular Technology , 2022
2022
Cited alongside, same era.
P. Wang, L. Shi, B. Chen, Z. Hu, J. Qiao, and Q. Dong, “Pursuing 3-D scene structures with optical satellite images from affine reconstruction to Euclidean reconstruction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Fan, F. Wang, N. Wang, and Z.-X. Zhang, “Fully sparse 3d object detection,” Advances in Neural Information Processing Systems , vol. 35, pp. 351–363, 2022
2022
Cited alongside, same era.
H. Wu, J. Deng, C. Wen, X. Li, C. Wang, and J. Li, “CasA: A cascade attention network for 3-D object detection from LiDAR point clouds,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022
2022
Cited alongside, same era.
T. Wang, Z. Xinge, J. Pang, and D. Lin, “Probabilistic and geometric depth: Detecting objects in perspective,” in Conference on Robot Learning . PMLR, 2022, pp. 1475–1485
2022
Cited alongside, same era.
Y. Hu, Z. Ding, R. Ge, W. Shao, L. Huang, K. Li, and Q. Liu, “Afdetv2: Rethinking the necessity of the second stage for object detection from point clouds,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 969–979
2022
Cited alongside, same era.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal Sparse Convolutional Networks for 3D Object Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5428–5437
2022
Cited alongside, same era.
2023
Later among the works it cites.
L. Wang, X. Zhang, Z. Song, J. Bi, G. Zhang, H. Wei, L. Tang, L. Yang, J. Li, C. Jia, and L. Zhao, “Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 7, pp. 3781–3798, 2023
2023
Later among the works it cites.
Z. Song, H. Wei, C. Jia, Y. Xia, X. Li, and C. Zhang, “VP-Net: Voxels as Points for 3D Object Detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
L. Wang, Z. Song, X. Zhang, C. Wang, G. Zhang, L. Zhu, J. Li, and H. Liu, “SAT-GCN: Self-attention graph convolutional network-based 3D object detection for autonomous driving,” Knowledge-Based Systems , vol. 259, p. 110080, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, “Voxelnext: Fully sparse voxelnet for 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 674–21 683
2023
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2023
Later among the works it cites.
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su, and Z. Wang, “3-D HANet: A Flexible 3-D Heatmap Auxiliary Network for Object Detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–13, 2023
2023
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Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, “Largekernel3d: Scaling up kernels in 3d sparse cnns,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 488–13 498
2023
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H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He, and W. Ouyang, “Pvt-ssd: Single-stage 3d object detector with point-voxel transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 476–13 487
2023
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W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu, and X. Li, “Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, “Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection,” International Journal of Computer Vision , vol. 131, no. 2, pp. 531–551, 2023
2023
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X. Tian, M. Yang, Q. Yu, J. Yong, and D. Xu, “MedoidsFormer: A Strong 3D Object Detection Backbone by Exploiting Interaction with Adjacent Medoid Tokens,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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2024
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S. Xu, F. Li, Z. Song, J. Fang, S. Wang, and Z.-X. Yang, “Multi-sem fusion: multimodal semantic fusion for 3d object detection,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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J. Bi, H. Wei, G. Zhang, K. Yang, and Z. Song, “Dyfusion: Cross-attention 3d object detection with dynamic fusion,” IEEE Latin America Transactions , vol. 22, no. 2, pp. 106–112, 2024
2024
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L. Wang, X. Zhang, F. Zhao, C. Wu, Y. Wang, Z. Song, L. Yang, B. Xu, J. Li, and S. S. Ge, “Fuzzy-nms: Improving 3d object detection with fuzzy classification in nms,” IEEE Transactions on Intelligent Vehicles , 2024
2024
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