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Data augmentation has been widely adopted for object detection in 3D point clouds.
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Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: Surprisingly easy synthesis for instance detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1301–1310 (2017)
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Jouppi, N.P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 44th Annual International Symposium on Computer Architecture. pp. 1–12 (2017)
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Lemley, J., Bazrafkan, S., Corcoran, P.: Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5
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Ratner, A.J., Ehrenberg, H., Hussain, Z., Dunnmon, J., Ré, C.: Learning to compose domain-specific transformations for data augmentation. In: Advances in Neural Information Processing Systems. pp. 3239–3249 (2017)
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Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: Detectron (2018)
2018
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Fang, H.S., Sun, J., Wang, R., Gou, M., Li, Y.L., Lu, C.: Instaboost: Boosting instance segmentation via probability map guided copy-pasting. In: The IEEE International Conference on Computer Vision (2019)
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Ho, D., Liang, E., Stoica, I., Abbeel, P., Chen, X.: Population based augmentation: Efficient learning of augmentation policy schedules. In: International Conference on Machine Learning. pp. 2731–2741 (2019)
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Later among the works it cites.
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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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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. pp. 641–656 (2018)
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)
2018
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Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors 18
2018
Cited alongside, same era.
Yang, B., Liang, M., Urtasun, R.: Hdnet: Exploiting HD maps for 3d object detection. In: Proceedings of The 2nd Conference on Robot Learning. pp. 146–155 (2018)
2018
Cited alongside, same era.
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
Cited alongside, same era.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
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
Cited alongside, same era.
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation policies from data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Later among the works it cites.
Lim, S., Kim, I., Kim, T., Kim, C., Kim, S.: Fast autoaugment. In: Advances in Neural Information Processing Systems (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., Yang, R.: Iou loss for 2d/3d object detection. In: International Conference on 3D Vision (3DV). IEEE (2019)
2019
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Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T., Guo, J., Ngiam, J., Vasudevan, V.: End-to-end multi-view fusion for 3d object detection in lidar point clouds. In: Proceedings of the Conference on Robot Learning (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Li, R., Li, X., Heng, P.A., Fu, C.W.: Pointaugment: an auto-augmentation framework for point cloud classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6378–6387 (2020)
2020
Closest in time.
Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., Li, H.: Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10529–10538 (2020)
2020
Closest in time.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., Anguelov, D.: Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 2446–2454 (2020)
2020
Closest in time.