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Promising performance has been achieved for visual perception on the point cloud.
Lai, K., Fox, D.: Object recognition in 3d point clouds using web data and domain adaptation. The International Journal of Robotics Research 29
2010
Earlier work this paper cites.
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: European conference on computer vision. pp. 213–226. Springer (2010)
2010
Earlier work this paper cites.
Knopp, J., Prasad, M., Van Gool, L.: Scene cut: Class-specific object detection and segmentation in 3d scenes. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 180–187. IEEE (2011)
2011
Earlier work this paper cites.
Nan, L., Xie, K., Sharf, A.: A search-classify approach for cluttered indoor scene understanding. ACM Transactions on Graphics (TOG) 31
2012
Earlier work this paper cites.
Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. arXiv preprint arXiv:1410.5401 (2014)
2014
Earlier work this paper cites.
Song, S., Xiao, J.: Sliding shapes for 3d object detection in depth images. In: European conference on computer vision. pp. 634–651. Springer (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: NIPS (2015)
2015
Earlier work this paper cites.
Graham, B.: Sparse 3d convolutional neural networks. arXiv preprint arXiv:1505.02890 (2015)
2015
Earlier work this paper cites.
Gupta, S., Arbeláez, P., Girshick, R., Malik, J.: Aligning 3d models to rgb-d images of cluttered scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4731–4740 (2015)
2015
Earlier work this paper cites.
Li, Y., Dai, A., Guibas, L., Nießner, M.: Database-assisted object retrieval for real-time 3d reconstruction. In: Computer graphics forum. vol. 34, pp. 435–446. Wiley Online Library (2015)
2015
Earlier work this paper cites.
Rockafellar, R.T.: Convex analysis. Princeton university press (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Yuksel, C.: Sample elimination for generating poisson disk sample sets. In: Computer Graphics Forum. vol. 34, pp. 25–32. Wiley Online Library (2015)
2015
Earlier work this paper cites.
Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D-3D-Semantic Data for Indoor Scene Understanding. ArXiv e-prints (Feb 2017)
2017
Earlier work this paper cites.
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5828–5839 (2017)
2017
Earlier work this paper cites.
Litany, O., Remez, T., Freedman, D., Shapira, L., Bronstein, A., Gal, R.: Asist: automatic semantically invariant scene transformation. Computer Vision and Image Understanding 157
2017
Earlier work this paper cites.
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)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7167–7176 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31
2018
Cited alongside, same era.
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., Van Der Maaten, L.: Exploring the limits of weakly supervised pretraining. In: Proceedings of the European conference on computer vision (ECCV). pp. 181–196 (2018)
2018
Cited alongside, same era.
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: Splatnet: Sparse lattice networks for point cloud processing. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2530–2539 (2018)
2018
Cited alongside, same era.
Zhang, Z., Sun, B., Yang, H., Huang, Q.: H3dnet: 3d object detection using hybrid geometric primitives. In: European Conference on Computer Vision. pp. 311–329. Springer (2020)
2020
Later among the works it cites.
Zhu, X., Ma, Y., Wang, T., Xu, Y., Shi, J., Lin, D.: Ssn: Shape signature networks for multi-class object detection from point clouds. In: European Conference on Computer Vision. pp. 581–597. Springer (2020)
2020
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2021
Later among the works it cites.
Cui, Z., Li, C., Chen, N., Wei, G., Chen, R., Zhou, Y., Shen, D., Wang, W.: Tsegnet: an efficient and accurate tooth segmentation network on 3d dental model. Medical Image Analysis 69
2021
Later among the works it cites.
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Avetisyan, A., Dai, A., Nießner, M.: End-to-end cad model retrieval and 9dof alignment in 3d scans. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2551–2560 (2019)
2019
Cited alongside, same era.
Bucher, M., Vu, T.H., Cord, M., Pérez, P.: Zero-shot semantic segmentation. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3075–3084 (2019)
2019
Cited alongside, same era.
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1705–1714 (2019)
2019
Cited alongside, same era.
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2537–2546 (2019)
2019
Cited alongside, same era.
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
Cited alongside, same era.
Qin, C., You, H., Wang, L., Kuo, C.C.J., Fu, Y.: Pointdan: A multi-scale 3d domain adaption network for point cloud representation. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9621–9630 (2019)
2019
Cited alongside, same era.
Cui, Z., Li, C., Du, Z., Chen, N., Wei, G., Chen, R., Yang, L., Shen, D., Wang, W.: Structure-driven unsupervised domain adaptation for cross-modality cardiac segmentation. IEEE Transactions on Medical Imaging 40
2021
Later among the works it cites.
Hong, F., Zhou, H., Zhu, X., Li, H., Liu, Z.: Lidar-based panoptic segmentation via dynamic shifting network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13090–13099 (2021)
2021
Later among the works it cites.
Hou, J., Graham, B., Nießner, M., Xie, S.: Exploring data-efficient 3d scene understanding with contrastive scene contexts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15587–15597 (2021)
2021
Later among the works it cites.
Jiang, L., Shi, S., Tian, Z., Lai, X., Liu, S., Fu, C.W., Jia, J.: Guided point contrastive learning for semi-supervised point cloud semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6423–6432 (2021)
2021
Later among the works it cites.
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
Later among the works it cites.
Liu, H., Jia, J., Gong, N.Z.: Pointguard: Provably robust 3d point cloud classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6186–6195 (2021)
2021
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2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, H., Cong, Y., Litany, O., Gao, Y., Guibas, L.J.: 3dioumatch: Leveraging iou prediction for semi-supervised 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14615–14624 (2021)
2021
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Yuan, Y., Borrmann, D., Hou, J., Ma, Y., Nüchter, A., Schwertfeger, S.: Self-supervised point set local descriptors for point cloud registration. Sensors 21
2021
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Zeng, Y., Qian, Y., Zhu, Z., Hou, J., Yuan, H., He, Y.: Corrnet3d: Unsupervised end-to-end learning of dense correspondence for 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6052–6061 (2021)
2021
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Zhang, H., Ding, H.: Prototypical matching and open set rejection for zero-shot semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6974–6983 (2021)
2021
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2021
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Zhao, N., Chua, T.S., Lee, G.H.: Few-shot 3d point cloud semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8873–8882 (2021)
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)
2021
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Chen, R., Ma, Y., Liu, L., Chen, N., Cui, Z., Wei, G., Wang, W.: Semi-supervised anatomical landmark detection via shape-regulated self-training. Neurocomputing 471
2022
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