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Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset.
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Q. Huang, W. Wang, and U. Neumann, “Recurrent slice networks for 3d segmentation of point clouds,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2626–2635
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H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” Proceedings of the IEEE International Conference on Computer Vision , 2019
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L. Yi, W. Zhao, H. Wang, M. Sung, and L. J. Guibas, “Gspn: Generative shape proposal network for 3d instance segmentation in point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3947–3956
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B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, and N. Trigoni, “Learning object bounding boxes for 3d instance segmentation on point clouds,” in Advances in Neural Information Processing Systems , 2019, pp. 6737–6746
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M. Z. Hossain, F. Sohel, M. F. Shiratuddin, and H. Laga, ACM Computing Surveys (CsUR) , vol. 51, no. 6, pp. 1–36, 2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019
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P. Dai, Y. Zhang, Z. Li, S. Liu, and B. Zeng, “Neural point cloud rendering via multi-plane projection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 7830–7839
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2021
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T. Vu, K. Kim, T. M. Luu, X. T. Nguyen, and C. D. Yoo, “Softgroup for 3d instance segmentation on 3d point clouds,” in CVPR , 2022
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B. Li, K. Q. Weinberger, S. Belongie, V. Koltun, and R. Ranftl, “Language-driven semantic segmentation,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=RriDjddCLN
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L. Jiang, H. Zhao, S. Shi, S. Liu, C.-W. Fu, and J. Jia, “Pointgroup: Dual-set point grouping for 3d instance segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
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2020
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A. Milioto, J. Behley, C. McCool, and C. Stachniss, “Lidar panoptic segmentation for autonomous driving,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 8505–8512
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A. Cheraghian, S. Rahman, D. Campbell, and L. Petersson, “Transductive zero-shot learning for 3d point cloud classification,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 923–933
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I. Misra, R. Girdhar, and A. Joulin, “An End-to-End Transformer Model for 3D Object Detection,” in ICCV , 2021
2021
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C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 4904–4916
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2021
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C. Zhou, C. C. Loy, and B. Dai, “Extract free dense labels from clip,” in European Conference on Computer Vision (ECCV) , 2022
2022
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R. Zhang, Z. Guo, W. Zhang, K. Li, X. Miao, B. Cui, Y. Qiao, P. Gao, and H. Li, “Pointclip: Point cloud understanding by clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8552–8562
2022
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2022
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2022
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W. K. Fong, R. Mohan, J. V. Hurtado, L. Zhou, H. Caesar, O. Beijbom, and A. Valada, “Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3795–3802, 2022
2022
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X. Lai, J. Liu, L. Jiang, L. Wang, H. Zhao, S. Liu, X. Qi, and J. Jia, “Stratified transformer for 3d point cloud segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8500–8509
2022
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X. Zhou, R. Girdhar, A. Joulin, P. Krähenbühl, and I. Misra, “Detecting twenty-thousand classes using image-level supervision,” in ECCV , 2022
2022
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G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin, “Scaling open-vocabulary image segmentation with image-level labels,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVI . Springer, 2022, pp. 540–557
2022
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J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello, “Open-vocabulary panoptic segmentation with text-to-image diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 2955–2966
2023
Closest in time.
S. Peng, K. Genova, C. M. Jiang, A. Tagliasacchi, M. Pollefeys, and T. Funkhouser, “Openscene: 3d scene understanding with open vocabularies,” in CVPR , 2023
2023
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2023
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R. Ding, J. Yang, C. Xue, W. Zhang, S. Bai, and X. Qi, “Pla: Language-driven open-vocabulary 3d scene understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
2023
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2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Zeng, C. Jiang, J. Mao, J. Han, C. Ye, Q. Huang, D.-Y. Yeung, Z. Yang, X. Liang, and H. Xu, “Clip2: Contrastive language-image-point pretraining from real-world point cloud data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 244–15 253
2023
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