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3D object detection with multi-sensors is essential for an accurate and reliable perception system of autonomous driving and robotics.
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S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in
2019
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Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” in
2019
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G. P. Meyer, J. Charland, D. Hegde, A. Laddha, and C. Vallespi-Gonzalez, “Sensor fusion for joint 3d object detection and semantic segmentation,” in
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,” vol. 43, no. 8. IEEE, 2020, pp. 2647–2664
2020
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Z. Li, F. Wang, and N. Wang, “Lidar r-cnn: An efficient and universal 3d object detector,” in
2021
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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
2021
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2021
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2021
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2021
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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
2020
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L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai, and X. He, “Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module,” in
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
2020
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in
2020
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S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in
2020
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Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan, “End-to-end multi-view fusion for 3d object detection in lidar point clouds,” in
2020
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Y. Wang, A. Fathi, A. Kundu, D. A. Ross, C. Pantofaru, T. Funkhouser, and J. Solomon, “Pillar-based object detection for autonomous driving,” in
2020
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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
2021
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C. Wang, C. Ma, M. Zhu, and X. Yang, “Pointaugmenting: Cross-modal augmentation for 3d object detection,” in
2021
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S. Xu, D. Zhou, J. Fang, J. Yin, Z. Bin, and L. Zhang, “Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection,” in
2021
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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
2021
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T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in
2021
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C. R. Qi, Y. Zhou, M. Najibi, P. Sun, K. Vo, B. Deng, and D. Anguelov, “Offboard 3d object detection from point cloud sequences,” in
2021
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Y. Li, A. W. Yu, T. Meng, B. Caine, J. Ngiam, D. Peng, J. Shen, Y. Lu, D. Zhou, Q. V. Le
2022
Closest in time.
X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C.-L. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in
2022
Closest in time.
2022
Closest in time.
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
2022
Closest in time.