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Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots.
Feichtenhofer, C., Pinz, A., Zisserman, A.: Detect to track and track to detect. In: Proceedings of the IEEE international conference on computer vision. pp. 3038–3046 (2017)
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2017
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Kang, K., Li, H., Xiao, T., Ouyang, W., Yan, J., Liu, X., Wang, X.: Object detection in videos with tubelet proposal networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 727–735 (2017)
2017
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Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X., et al.: T-cnn: Tubelets with convolutional neural networks for object detection from videos. IEEE Transactions on Circuits and Systems for Video Technology 28
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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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Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
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Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 408–417 (2017)
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Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Deep feature flow for video recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2349–2358 (2017)
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Bertasius, G., Torresani, L., Shi, J.: Object detection in video with spatiotemporal sampling networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 331–346 (2018)
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Luo, W., Yang, B., Urtasun, R.: Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 3569–3577 (2018)
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Wang, S., Zhou, Y., Yan, J., Deng, Z.: Fully motion-aware network for video object detection. In: Proceedings of the European conference on computer vision (ECCV). pp. 542–557 (2018)
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Xiao, F., Lee, Y.J.: Video object detection with an aligned spatial-temporal memory. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 485–501 (2018)
2018
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Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors 18
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)
2018
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Deng, J., Pan, Y., Yao, T., Zhou, W., Li, H., Mei, T.: Relation distillation networks for video object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7023–7032 (2019)
2019
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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)
2019
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2019
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2021
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Jiao, L., Zhang, R., Liu, F., Yang, S., Hou, B., Li, L., Tang, X.: New generation deep learning for video object detection: A survey. IEEE Transactions on Neural Networks and Learning Systems (2021)
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)
2021
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Luo, C., Yang, X., Yuille, A.: Exploring simple 3d multi-object tracking for autonomous driving. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10488–10497 (2021)
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Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3d object detection in point clouds. In: proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9277–9286 (2019)
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)
2019
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Wu, H., Chen, Y., Wang, N., Zhang, Z.: Sequence level semantics aggregation for video object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9217–9225 (2019)
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)
2019
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Chen, Y., Cao, Y., Hu, H., Wang, L.: Memory enhanced global-local aggregation for video object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10337–10346 (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Han, M., Wang, Y., Chang, X., Qiao, Y.: Mining inter-video proposal relations for video object detection. In: European conference on computer vision. pp. 431–446. Springer (2020)
2020
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2020
Cited alongside, same era.
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)
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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Sheng, H., Cai, S., Liu, Y., Deng, B., Huang, J., Hua, X.S., Zhao, M.J.: Improving 3d object detection with channel-wise transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2743–2752 (2021)
2021
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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)
2021
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Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.: Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems 34
2021
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Wang, J., Chakraborty, R., Yu, S.X.: Spatial transformer for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 1–1 (2021). https://doi.org/10.1109/TPAMI.2021.3070341
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)
2021
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Yuan, Z., Song, X., Bai, L., Wang, Z., Ouyang, W.: Temporal-channel transformer for 3d lidar-based video object detection for autonomous driving. IEEE Transactions on Circuits and Systems for Video Technology pp. 1–1 (2021). https://doi.org/10.1109/TCSVT.2021.3082763
2021
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