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3D object part segmentation is essential in computer vision applications.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International journal of computer vision 60
2004
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Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3D Mesh Segmentation and Labeling. ACM Transactions on Graphics 29
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Varadarajan, K.M., Vincze, M.: Object part segmentation and classification in range images for grasping. In: 2011 15th International Conference on Advanced Robotics (ICAR). pp. 21–27. IEEE (2011)
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2015
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Yi, L., Kim, V.G., Ceylan, D., Shen, I.C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., Guibas, L.: A scalable active framework for region annotation in 3d shape collections. SIGGRAPH Asia (2016)
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Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30
2017
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Dai, A., Nießner, M.: 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 452–468 (2018)
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Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5356–5364 (2019)
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Jaritz, M., Gu, J., Su, H.: Multi-view pointnet for 3d scene understanding. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) pp. 3995–4003 (2019), https://api.semanticscholar.org/CorpusID:203593088
2019
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2019
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Mo, K., Zhu, S., Chang, A.X., Yi, L., Tripathi, S., Guibas, L.J., Su, H.: Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 909–918 (2019)
2019
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Zhu, J., Zhang, Y., Guo, J., Liu, H., Liu, M., Liu, Y., Guo, Y.: Label transfer between images and 3d shapes via local correspondence encoding. Comput. Aided Geom. Des. 71
2019
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Chen, N., Liu, L., Cui, Z., Chen, R., Ceylan, D., Tu, C., Wang, W.: Unsupervised learning of intrinsic structural representation points. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9121–9130 (2020)
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Gadelha, M., RoyChowdhury, A., Sharma, G., Kalogerakis, E., Cao, L., Learned-Miller, E., Wang, R., Maji, S.: Label-efficient learning on point clouds using approximate convex decompositions. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16. pp. 473–491. Springer (2020)
2020
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Wang, L., Li, X., Fang, Y.: Few-shot learning of part-specific probability space for 3d shape segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
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Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., et al.: Sapien: A simulated part-based interactive environment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11097–11107 (2020)
2020
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2021
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9650–9660 (2021)
2021
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Deng, S., Xu, X., Wu, C., Chen, K., Jia, K.: 3d affordancenet: A benchmark for visual object affordance understanding. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1778–1787 (2021)
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
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Zhao, L., Lu, J., Zhou, J.: Similarity-aware fusion network for 3d semantic segmentation. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 1585–1592 (2021), https://api.semanticscholar.org/CorpusID:235732071
Liu, M., Zhu, Y., Cai, H., Han, S., Ling, Z., Porikli, F., Su, H.: Partslip: Low-shot part segmentation for 3d point clouds via pretrained image-language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21736–21746 (2023)
2023
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Liu, W., Mao, J., Hsu, J., Hermans, T., Garg, A., Wu, J.: Composable part-based manipulation. In: 7th Annual Conference on Robot Learning (2023), https://openreview.net/forum?id=o-K3HVUeEw
2023
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2023
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Nguyen, P.D.A., Ngo, T.D., Gan, C., Kalogerakis, E., Tran, A., Pham, C., Nguyen, K.: Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance (2023)
2023
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2021
Cited alongside, same era.
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
Cited alongside, same era.
He, J., Yang, S., Yang, S., Kortylewski, A., Yuan, X., Chen, J.N., Liu, S., Yang, C., Yu, Q., Yuille, A.: Partimagenet: A large, high-quality dataset of parts. In: European Conference on Computer Vision. pp. 128–145. Springer (2022)
2022
Cited alongside, same era.
Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.N., et al.: Grounded language-image pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10965–10975 (2022)
2022
Cited alongside, same era.
Li, Y., Upadhyay, U., Habib Slim, A.A., Arpit Prajapati, S.P., Wonka, P., Elhoseiny, M.: 3d compat: Composition of materials on parts of 3d things (eccv 2022). ECCV (2022)
2022
Cited alongside, same era.
Liu, X., Xu, X., Rao, A., Gan, C., Yi, L.: Autogpart: Intermediate supervision search for generalizable 3d part segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11624–11634 (2022)
2022
Cited alongside, same era.
Qian, G., Li, Y., Peng, H., Mai, J., Hammoud, H., Elhoseiny, M., Ghanem, B.: Pointnext: Revisiting pointnet++ with improved training and scaling strategies. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Sharma, G., Yin, K., Maji, S., Kalogerakis, E., Litany, O., Fidler, S.: Mvdecor: Multi-view dense correspondence learning for fine-grained 3d segmentation. In: European Conference on Computer Vision. pp. 550–567. Springer (2022)
2022
Cited alongside, same era.
Vu, T., Kim, K., Luu, T.M., Nguyen, T., Yoo, C.D.: Softgroup for 3d instance segmentation on point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2708–2717 (2022)
2022
Cited alongside, same era.
Peng, S., Genova, K., Jiang, C., Tagliasacchi, A., Pollefeys, M., Funkhouser, T., et al.: Openscene: 3d scene understanding with open vocabularies. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 815–824 (2023)
2023
Later among the works it cites.
Ramanathan, V., Kalia, A., Petrovic, V., Wen, Y., Zheng, B., Guo, B., Wang, R., Marquez, A., Kovvuri, R., Kadian, A., et al.: Paco: Parts and attributes of common objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7141–7151 (2023)
2023
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2023
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2023
Later among the works it cites.
2023
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2023
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Xue, Y., Chen, N., Liu, J., Sun, W.: Zerops: High-quality cross-modal knowledge transfer for zero-shot 3d part segmentation (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhu, X., Zhang, R., He, B., Guo, Z., Zeng, Z., Qin, Z., Zhang, S., Gao, P.: Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2639–2650 (2023)
2023
Later among the works it cites.
Cen, J., Zhou, Z., Fang, J., Shen, W., Xie, L., Jiang, D., Zhang, X., Tian, Q., et al.: Segment anything in 3d with nerfs. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Kim, H., Sung, M.: Partstad: 2d-to-3d part segmentation task adaptation (2024)
2024
Closest in time.