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Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps.
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 . Springer, 2016, pp. 21–37
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B. Li, “3d fully convolutional network for vehicle detection in point cloud,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 1513–1518
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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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” vol. 30, 2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
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Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
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J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 1–8
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 918–927
2018
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G. Brazil and X. Liu, “M3d-rpn: Monocular 3d region proposal network for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9287–9296
2019
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P. Li, X. Chen, and S. Shen, “Stereo r-cnn based 3d object detection for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7644–7652
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
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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 proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9277–9286
2019
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S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 770–779
2019
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Z. Wang and K. Jia, “Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1742–1749
2019
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K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang et al. , “Hybrid task cascade for instance segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4974–4983
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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
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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
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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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2446–2454
2020
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J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 194–210
2020
Cited alongside, same era.
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
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Y. Li, Y. Chen, J. He, and Z. Zhang, “Densely constrained depth estimator for monocular 3d object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 718–734
2022
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2022
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Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” in European conference on computer vision . Springer, 2022, pp. 1–18
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2022
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2020
Cited alongside, same era.
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
Cited alongside, same era.
J. H. Yoo, Y. Kim, J. Kim, and J. W. Choi, “3d-cvf: Generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII 16 . Springer, 2020, pp. 720–736
2020
Cited alongside, same era.
T. Huang, Z. Liu, X. Chen, and X. Bai, “Epnet: Enhancing point features with image semantics for 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16 . Springer, 2020, pp. 35–52
2020
Cited alongside, same era.
S. Pang, D. Morris, and H. Radha, “Clocs: Camera-lidar object candidates fusion for 3d object detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 386–10 393
2020
Cited alongside, same era.
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
Cited alongside, same era.
C. Wang, C. Ma, M. Zhu, and X. Yang, “Pointaugmenting: Cross-modal augmentation for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 794–11 803
2021
Cited alongside, same era.
Z. Liu, D. Zhou, F. Lu, J. Fang, and L. Zhang, “Autoshape: Real-time shape-aware monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 641–15 650
2021
Cited alongside, same era.
Y. Zhang, J. Lu, and J. Zhou, “Objects are different: Flexible monocular 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3289–3298
2021
Cited alongside, same era.
Y. Wang, V. C. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” in Conference on Robot Learning . PMLR, 2022, pp. 180–191
2022
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Y. Liu, T. Wang, X. Zhang, and J. Sun, “Petr: Position embedding transformation for multi-view 3d object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 531–548
2022
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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
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Z. Tian, X. Chu, X. Wang, X. Wei, and C. Shen, “Fully convolutional one-stage 3d object detection on lidar range images,” vol. 35, 2022, pp. 34 899–34 911
2022
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Y. Li, Y. Chen, X. Qi, Z. Li, J. Sun, and J. Jia, “Unifying voxel-based representation with transformer for 3d object detection,” vol. 35, 2022, pp. 18 442–18 455
2022
Later among the works it cites.
Y. Li, A. W. Yu, T. Meng, B. Caine, J. Ngiam, D. Peng, J. Shen, Y. Lu, D. Zhou, Q. V. Le et al. , “Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 182–17 191
2022
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Z. Chen, Z. Li, S. Zhang, L. Fang, Q. Jiang, F. Zhao, B. Zhou, and H. Zhao, “Autoalign: Pixel-instance feature aggregation for multi-modal 3d object detection,” 2022
2022
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Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” in 2023 IEEE international conference on robotics and automation (ICRA) . IEEE, 2023, pp. 2774–2781
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 674–21 683
2023
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Z. Liu, X. Yang, H. Tang, S. Yang, and S. Han, “Flatformer: Flattened window attention for efficient point cloud transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1200–1211
2023
Closest in time.
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
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2023
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J. He, Y. Chen, N. Wang, and Z. Zhang, “3d video object detection with learnable object-centric global optimization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5106–5115
2023
Closest in time.
Y. Wang, Y. Chen, and Z. Zhang, “Frustumformer: Adaptive instance-aware resampling for multi-view 3d detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5096–5105
2023
Closest in time.
Y. Li, Z. Ge, G. Yu, J. Yang, Z. Wang, Y. Shi, J. Sun, and Z. Li, “Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1477–1485
2023
Closest in time.
C. Yang, Y. Chen, H. Tian, C. Tao, X. Zhu, Z. Zhang, G. Huang, H. Li, Y. Qiao, L. Lu et al. , “Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 830–17 839
2023
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Z. Wang, Z. Huang, J. Fu, N. Wang, and S. Liu, “Object as query: Lifting any 2d object detector to 3d detection,” pp. 3791–3800, 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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X. Chen, T. Zhang, Y. Wang, Y. Wang, and H. Zhao, “Futr3d: A unified sensor fusion framework for 3d detection,” in proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 172–181
2023
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Y. Xie, C. Xu, M.-J. Rakotosaona, P. Rim, F. Tombari, K. Keutzer, M. Tomizuka, and W. Zhan, “Sparsefusion: Fusing multi-modal sparse representations for multi-sensor 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 591–17 602
2023
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X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao et al. , “Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 524–17 534
2023
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