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Real-time and high-performance 3D object detection is of critical importance for autonomous driving.
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Liang, M., Yang, B., Wang, S., Urtasun, R.: Deep continuous fusion for multi-sensor 3d object detection. In: Proceedings of the European conference on computer vision (ECCV). pp. 641–656 (2018)
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Zhou, Y., Tuzel, O.: Voxelnet: End-to-end learning for point cloud based 3d object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4490–4499 (2018)
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Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12697–12705 (2019)
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Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: Multi-task multi-sensor fusion for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7345–7353 (2019)
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Shi, S., Wang, X., Li, H.: Pointrcnn: 3d object proposal generation and detection from point cloud. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 770–779 (2019)
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Yang, Z., Sun, Y., Liu, S., Shen, X., Jia, J.: Std: Sparse-to-dense 3d object detector for point cloud. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1951–1960 (2019)
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Yoo, J.H., Kim, Y., Kim, J., Choi, J.W.: 3d-cvf: Generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection. In: European Conference on Computer Vision. pp. 720–736. Springer (2020)
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Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12993–13000 (2020)
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Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., Li, H.: Voxel r-cnn: Towards high performance voxel-based 3d object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)
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2019
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Bewley, A., Sun, P., Mensink, T., Anguelov, D., Sminchisescu, C.: Range conditioned dilated convolutions for scale invariant 3d object detection. In: Conference on Robot Learning (CoRL) (2020)
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Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621–11631 (2020)
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Chen, Q., Sun, L., Cheung, E., Yuille, A.L.: Every view counts: Cross-view consistency in 3d object detection with hybrid-cylindrical-spherical voxelization. Advances in Neural Information Processing Systems 33
2020
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Chen, Q., Sun, L., Wang, Z., Jia, K., Yuille, A.: Object as hotspots: An anchor-free 3d object detection approach via firing of hotspots. In: European conference on computer vision. pp. 68–84. Springer (2020)
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He, C., Zeng, H., Huang, J., Hua, X.S., Zhang, L.: Structure aware single-stage 3d object detection from point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11873–11882 (2020)
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Hu, P., Ziglar, J., Held, D., Ramanan, D.: What you see is what you get: Exploiting visibility for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11001–11009 (2020)
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Liu, Z., Zhao, X., Huang, T., Hu, R., Zhou, Y., Bai, X.: Tanet: Robust 3d object detection from point clouds with triple attention. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 11677–11684 (2020)
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Fan, L., Xiong, X., Wang, F., Wang, N., Zhang, Z.: Rangedet: In defense of range view for lidar-based 3d object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2918–2927 (2021)
2021
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2021
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Li, Z., Wang, F., Wang, N.: Lidar r-cnn: An efficient and universal 3d object detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7546–7555 (2021)
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Liang, Z., Zhang, Z., Zhang, M., Zhao, X., Pu, S.: Rangeioudet: Range image based real-time 3d object detector optimized by intersection over union. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7140–7149 (2021)
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Mao, J., Niu, M., Bai, H., Liang, X., Xu, H., Xu, C.: Pyramid r-cnn: Towards better performance and adaptability for 3d object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2723–2732 (2021)
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Noh, J., Lee, S., Ham, B.: Hvpr: Hybrid voxel-point representation for single-stage 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14605–14614 (2021)
2021
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Qi, C.R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., Anguelov, D.: Offboard 3d object detection from point cloud sequences. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6134–6144 (2021)
2021
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Sun, P., Wang, W., Chai, Y., Elsayed, G., Bewley, A., Zhang, X., Sminchisescu, C., Anguelov, D.: Rsn: Range sparse net for efficient, accurate lidar 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5725–5734 (2021)
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Yang, Z., Zhou, Y., Chen, Z., Ngiam, J.: 3d-man: 3d multi-frame attention network for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1863–1872 (2021)
2021
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Yin, T., Zhou, X., Krahenbuhl, P.: Center-based 3d object detection and tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11784–11793 (2021)
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Zheng, W., Tang, W., Chen, S., Jiang, L., Fu, C.W.: Cia-ssd: Confident iou-aware single-stage object detector from point cloud. In: AAAI (2021)
2021
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Zheng, W., Tang, W., Jiang, L., Fu, C.W.: Se-ssd: Self-ensembling single-stage object detector from point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14494–14503 (2021)
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Zhu, X., Zhou, H., Wang, T., Hong, F., Li, W., Ma, Y., Li, H., Yang, R., Lin, D.: Cylindrical and asymmetrical 3d convolution networks for lidar-based perception. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
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