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Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds.
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Golovinskiy, A., Kim, V.G., Funkhouser, T.: Shape-based recognition of 3d point clouds in urban environments. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 2154–2161. IEEE (2009)
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Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 2155–2162. IEEE (2010)
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Jetley, S., Sapienza, M., Golodetz, S., Torr, P.H.S.: Straight to shapes: Real-time detection of encoded shapes. CVPR pp. 4207–4216 (2016)
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Kasaei, S.H., Tomé, A.M., Lopes, L.S., Oliveira, M.: Good: A global orthographic object descriptor for 3d object recognition and manipulation. Pattern Recognition Letters 83
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Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1907–1915 (2017)
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Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
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Pham, C.C., Jeon, J.W.: Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks. Signal Processing: Image Communication 53
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Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 652–660 (2017)
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Xu, D., Anguelov, D., Jain, A.: Pointfusion: Deep sensor fusion for 3d bounding box estimation. CVPR pp. 244–253 (2017)
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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 Conference on Computer Vision and Pattern Recognition. pp. 12697–12705 (2019)
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Li, P., Chen, X., Shen, S.: Stereo r-cnn based 3d object detection for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7644–7652 (2019)
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Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: Multi-task multi-sensor fusion for 3d object detection. CVPR pp. 7337–7345 (2019)
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Liu, L., Lu, J., Xu, C., Tian, Q., Zhou, J.: Deep fitting degree scoring network for monocular 3d object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1057–1066 (2019)
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Ku, J., Mozifian, M., Lee, J., Harakeh, A., Waslander, S.L.: Joint 3d proposal generation and object detection from view aggregation. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1–8. IEEE (2018)
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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)
2018
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Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 918–927 (2018)
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Xu, B., Chen, Z.: Multi-level fusion based 3d object detection from monocular images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2345–2353 (2018)
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Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors 18
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Yang, B., Liang, M., Urtasun, R.: Hdnet: Exploiting hd maps for 3d object detection. In: Conference on Robot Learning. pp. 146–155 (2018)
2018
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Yang, B., Luo, W., Urtasun, R.: Pixor: Real-time 3d object detection from point clouds. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 7652–7660 (2018)
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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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 Conference on Computer Vision and Pattern Recognition. pp. 770–779 (2019)
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Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., Weinberger, K.Q.: Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8445–8453 (2019)
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Xu, Y., Zhu, X., Shi, J., Zhang, G., Bao, H., Li, H.: Depth completion from sparse lidar data with depth-normal constraints. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2811–2820 (2019)
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Tai, W., Xinge, Z., Dahua, L.: Reconfigurable voxels: A new representation for lidar-based point clouds. arXiv preprint arXiv:2020 (2020)
2020
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