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This paper presents a novel framework for robust 3D object detection from point clouds via cross-modal hallucination.
Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1951–1960
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J. Hoffman, S. Gupta, and T. Darrell, “Learning with side information through modality hallucination,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 826–834
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S. Gupta, J. Hoffman, and J. Malik, “Cross modal distillation for supervision transfer,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2827–2836
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S. Lee, S. Kang, S.-C. Kim, and J.-E. Lee, “Radar cross section measurement with 77 ghz automotive fmcw radar,” in 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) . IEEE, 2016, pp. 1–6
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D. Xu, W. Ouyang, E. Ricci, X. Wang, and N. Sebe, “Learning cross-modal deep representations for robust pedestrian detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5363–5371
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J. Lezama, Q. Qiu, and G. Sapiro, “Not afraid of the dark: Nir-vis face recognition via cross-spectral hallucination and low-rank embedding,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 6628–6637
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C. Choi, S. Kim, and K. Ramani, “Learning hand articulations by hallucinating heat distribution,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3104–3113
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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,” Advances in neural information processing systems , vol. 30, 2017
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E. Bel Kamel, A. Peden, and P. Pajusco, “Rcs modeling and measurements for automotive radar applications in the w band,” in 2017 11th European Conference on Antennas and Propagation (EUCAP) , 2017, pp. 2445–2449
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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
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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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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
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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 International Conference on Computer Vision , 2019
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M. Meyer and G. Kuschk, “Deep learning based 3d object detection for automotive radar and camera,” in 2019 16th European Radar Conference (EuRAD) . IEEE, 2019, pp. 133–136
2019
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C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 873–11 882
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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
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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
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J. Noh, S. Lee, and B. Ham, “Hvpr: Hybrid voxel-point representation for single-stage 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 605–14 614
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——, “Automotive radar dataset for deep learning based 3d object detection,” in 2019 16th European Radar Conference (EuRAD) , 2019, pp. 129–132
2019
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Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou, and X. Bai, “Tanet: Robust 3d object detection from point clouds with triple attention,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, pp. 11 677–11 684, Apr. 2020. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/6837
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
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
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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.
K. Bansal, K. Rungta, S. Zhu, and D. Bharadia, “Pointillism: Accurate 3d bounding box estimation with multi-radars,” in Proceedings of the 18th Conference on Embedded Networked Sensor Systems , 2020, pp. 340–353
2020
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M. R. U. Saputra, P. P. de Gusmao, C. X. Lu, Y. Almalioglu, S. Rosa, C. Chen, J. Wahlström, W. Wang, A. Markham, and N. Trigoni, “Deeptio: A deep thermal-inertial odometry with visual hallucination,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1672–1679, 2020
2020
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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
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2021
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S. Ren, Y. Du, J. Lv, G. Han, and S. He, “Learning from the master: Distilling cross-modal advanced knowledge for lip reading,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 13 325–13 333
2021
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Y. Wang, Z. Jiang, X. Gao, J.-N. Hwang, G. Xing, and H. Liu, “Rodnet: Radar object detection using cross-modal supervision,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 504–513
2021
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A. Zhang, F. E. Nowruzi, and R. Laganiere, “Raddet: Range-azimuth-doppler based radar object detection for dynamic road users,” in 2021 18th Conference on Robots and Vision (CRV) . IEEE, 2021, pp. 95–102
2021
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K. Qian, S. Zhu, X. Zhang, and L. E. Li, “Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 444–453
2021
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Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan, and Y. Guo, “Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 953–18 962
2022
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A. Palffy, E. Pool, S. Baratam, J. F. Kooij, and D. M. Gavrila, “Multi-class road user detection with 3+ 1d radar in the view-of-delft dataset,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4961–4968, 2022
2022
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W. Zheng, M. Hong, L. Jiang, and C.-W. Fu, “Boosting 3d object detection by simulating multimodality on point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 638–13 647
2022
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Z. Yuan, X. Yan, Y. Liao, Y. Guo, G. Li, S. Cui, and Z. Li, “X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8563–8573
2022
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