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LiDAR-based fully sparse architecture has garnered increasing attention.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,” NeurIPS , vol. 28, 2015
2015
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2016
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in NeurIPS , 2017
2017
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in CVPR , 2017
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” in ICCV , 2017
2017
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Y. Zhou and O. Tuzel, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection,” in CVPR , 2018
2018
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B. Yang, W. Luo, and R. Urtasun, “PIXOR: Real-time 3D Object Detection from Point Clouds,” in CVPR , 2018
2018
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Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely Embedded Convolutional Detection,” Sensors , vol. 18, no. 10, 2018
2018
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C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep Hough Voting for 3D Object Detection in Point Clouds,” in ICCV , 2019
2019
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast Encoders for Object Detection from Point Clouds,” in CVPR , 2019
2019
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X. Zhou, D. Wang, and P. Krähenbühl, “Objects as Points,” arXiv preprint arXiv:1904.07850 , 2019
2019
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S. Shi, X. Wang, and H. Li, “PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud,” in CVPR , 2019
2019
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Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: Fully Convolutional One-Stage Object Detection,” in ICCV , 2019
2019
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2019
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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 , 2020
2020
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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 CVPR , 2020
2020
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3DSSD: Point-based 3D Single Stage Object Detector,” in CVPR , 2020
2020
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S. Zhang, C. Chi, Y. Yao, Z. Lei, and S. Z. Li, “Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection,” in CVPR , 2020
2020
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2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end Object Detection with Transformers,” in ECCV , 2020
2020
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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 CVPR , 2020
2020
Cited alongside, same era.
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 CVPR , 2020
2020
Cited alongside, same era.
M. Contributors, “MMDetection3D: OpenMMLab Next-generation Platform for General 3D Object Detection,” https://github.com/open-mmlab/mmdetection3d , 2020
2020
Cited alongside, same era.
Q. Chen, L. Sun, E. Cheung, and A. L. Yuille, “Every view counts: Cross-view Consistency in 3D Object Detection with Hybrid-cylindrical-spherical Voxelization,” NeurIPS , 2020
2020
Cited alongside, same era.
L. Fan, Z. Pang, T. Zhang, Y.-X. Wang, H. Zhao, F. Wang, N. Wang, and Z. Zhang, “Embracing Single Stride 3D Object Detector with Sparse Transformer,” in CVPR , 2022
2022
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C. He, R. Li, S. Li, and L. Zhang, “Voxel Set Transformer: A Set-to-set Approach to 3D Object Detection from Point Clouds,” in CVPR , 2022
2022
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P. Sun, M. Tan, W. Wang, C. Liu, F. Xia, Z. Leng, and D. Anguelov, “SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds,” in ECCV , 2022
2022
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S. Dong, H. Wang, T. Xu, X. Xu, J. Wang, Z. Bian, Y. Wang, J. Li et al. , “MsSVT: Mixed-scale sparse voxel transformer for 3d object detection on point clouds,” NeurIPS , 2022
2022
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T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis, and D. Manocha, “M3DETR: Multi-Representation, Multi-Scale, Mutual-Relation 3D Object Detection With Transformers,” in WACV , 2022
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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,” in ECCV , 2020
2020
Cited alongside, same era.
T. Yin, X. Zhou, and P. Krähenbühl, “Center-based 3D Object Detection and Tracking,” in CVPR , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu, and C. Xu, “Voxel Transformer for 3D Object Detection,” in ICCV , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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 , 2021
2021
Cited alongside, same era.
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu, and C. Xu, “Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection,” in ICCV , 2021
2021
Cited alongside, same era.
P. Sun, W. Wang, Y. Chai, G. Elsayed, A. Bewley, X. Zhang, C. Sminchisescu, and D. Anguelov, “RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection,” in CVPR , 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He, and D. Cai, “Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph,” in ECCV . Springer, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Deng, Z. Liang, L. Sun, and K. Jia, “Vista: Boosting 3d object detection via dual cross-view spatial attention,” in CVPR , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal Sparse Convolutional Networks for 3D Object Detection,” in CVPR , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Fan, Y. Yang, F. Wang, N. Wang, and Z. Zhang, “Super Sparse 3D Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, “VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking,” in CVPR , 2023
2023
Closest in time.
Z. Liu, X. Yang, H. Tang, S. Yang, and S. Han, “FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer,” in CVPR , 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 CVPR , 2023
2023
Closest in time.
L. Fan, Y. Yang, Y. Mao, F. Wang, Y. Chen, N. Wang, and Z. Zhang, “Once detected, never lost: Surpassing human performance in offline lidar based 3d object detection,” in ICCV , 2023
2023
Closest in time.
J. He, Y. Chen, N. Wang, and Z. Zhang, “3d video object detection with learnable object-centric global optimization,” in CVPR , 2023, pp. 5106–5115
2023
Closest in time.
B. Zhu, Z. Wang, S. Shi, H. Xu, L. Hong, and H. Li, “ConQueR: Query Contrast Voxel-DETR for 3D Object Detection,” in CVPR , 2023
2023
Closest in time.