One Class One Click: Quasi scene-level weakly supervised point cloud semantic segmentation with active learning
Author:
Publisher
Elsevier BV
Subject
Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics
Reference51 articles.
1. Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J., 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 77–85.
2. Cheng, M., Hui, L., Xie, J., Yang, J., 2021. SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, No. 2. pp. 1140–1147.
3. Chibane, J., Engelmann, F., Anh Tran, T., Pons-Moll, G., 2022. Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation using Bounding Boxes. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (Eds.), Computer Vision – ECCV 2022. pp. 681–699.
4. Deng, S., Dong, Q., Liu, B., Hu, Z., 2022a. Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds. In: 2022 International Conference on Robotics and Automation. ICRA, pp. 9214–9220.
5. A weakly supervised framework for real-world point cloud classification;Deng;Comput. Graph.,2022
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