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Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks.
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9621–9630
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Z. Han, X. Wang, Y.-S. Liu, and M. Zwicker, “Multi-angle point cloud-vae: Unsupervised feature learning for 3d point clouds from multiple angles by joint self-reconstruction and half-to-half prediction,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE, 2019, pp. 10 441–10 450
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M. A. Uy, Q.-H. Pham, B.-S. Hua, D. T. Nguyen, and S.-K. Yeung, “Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,” in International Conference on Computer Vision (ICCV) , 2019
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Y. Liu, B. Fan, S. Xiang, and C. Pan, “Relation-shape convolutional neural network for point cloud analysis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8895–8904
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L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 11, pp. 4037–4058, 2021
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2021
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2021
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2021
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Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II . Springer, 2022, pp. 604–621
2022
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H. Liu, M. Cai, and Y. J. Lee, “Masked discrimination for self-supervised learning on point clouds,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II . Springer, 2022, pp. 657–675
2022
Closest in time.