{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:56:08Z","timestamp":1762325768059,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272019"],"award-info":[{"award-number":["62272019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these problems to the lack of attention to local details in the global optimization methods used for the task. Thus, we propose a new model, called PA-NET, to guide the network to pay more attention to local structures. Specifically, we first use textual embedding to assist in training a robust point assignment network, enabling the transformation of global optimization into the co-optimization of local and global aspects. Then, we design a novel plug-in module using the assignment network and introduce a new loss function to guide the network\u2019s attention towards local structures. Numerous experiments were conducted, and the quantitative results demonstrate that our method achieves novel performance on different datasets. Additionally, the visualization results show that our method efficiently resolves the issue of poor local structures in the generated point cloud.<\/jats:p>","DOI":"10.3390\/e25121588","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T03:32:03Z","timestamp":1701055923000},"page":"1588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Part-Aware Point Cloud Completion through Multi-Modal Part Segmentation"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9744-7040","authenticated-orcid":false,"given":"Fuyang","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runze","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6351-2538","authenticated-orcid":false,"given":"Xiaohui","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wen, X., Li, T., Han, Z., and Liu, Y.S. (2020, January 13\u201319). Point cloud completion by skip-attention network with hierarchical folding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00201"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s00371-007-0167-y","article-title":"A robust hole-filling algorithm for triangular mesh","volume":"23","author":"Zhao","year":"2007","journal-title":"Vis. Comput."},{"key":"ref_3","unstructured":"Sorkine, O., and Cohen-Or, D. (2004, January 7\u20139). Least-squares meshes. Proceedings of the Shape Modeling Applications, Genova, Italy."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Han, X., Li, Z., Huang, H., Kalogerakis, E., and Yu, Y. (2017, January 22\u201329). High-resolution shape completion using deep neural networks for global structure and local geometry inference. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.19"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Litany, O., Bronstein, A., Bronstein, M., and Makadia, A. (2018, January 18\u201323). Deformable shape completion with graph convolutional autoencoders. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00202"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, C., Shen, Y., and Tian, D. (2018, January 18\u201323). Foldingnet: Point cloud auto-encoder via deep grid deformation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00029"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, Y., Xu, J., Ni, F., and Le, X. (2020, January 14\u201319). Pf-net: Point fractal network for 3d point cloud completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"ref_8","first-page":"6320","article-title":"Snowflake point deconvolution for point cloud completion and generation with skip-transformer","volume":"45","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., and Hebert, M. (2018, January 5\u20138). Pcn: Point completion network. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00088"},{"key":"ref_10","unstructured":"Chen, X., Chen, B., and Mitra, N.J. (2019). Unpaired point cloud completion on real scans using adversarial training. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gu, J., Ma, W.C., Manivasagam, S., Zeng, W., Wang, Z., Xiong, Y., Su, H., and Urtasun, R. (2020, January 23\u201328). Weakly-supervised 3D shape completion in the wild. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part V 16.","DOI":"10.1007\/978-3-030-58558-7_17"},{"key":"ref_12","first-page":"16119","article-title":"Skeleton-bridged point completion: From global inference to local adjustment","volume":"33","author":"Nie","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","unstructured":"Li, R., Li, X., Fu, C.W., Cohen-Or, D., and Heng, P.A. (November, January 27). Pu-gan: A point cloud upsampling adversarial network. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zong, D., Sun, S., and Zhao, J. (2021, January 2\u20139). ASHF-Net: Adaptive sampling and hierarchical folding network for robust point cloud completion. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i4.16478"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yan, Q., and Xiao, C. (2020, January 23\u201328). Detail preserved point cloud completion via separated feature aggregation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXV 16.","DOI":"10.1007\/978-3-030-58595-2_31"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, X., Ang, M.H., and Lee, G.H. (2020, January 14\u201319). Cascaded refinement network for point cloud completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00087"},{"key":"ref_17","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 7\u201312). 3D shapenets: A deep representation for volumetric shapes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mo, K., Zhu, S., Chang, A.X., Yi, L., Tripathi, S., Guibas, L.J., and Su, H. (2019, January 15\u201320). Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00100"},{"key":"ref_19","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., and Guibas, L.J. (November, January 27). Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yu, F., Liu, K., Zhang, Y., Zhu, C., and Xu, K. (2019, January 15\u201320). Partnet: A recursive part decomposition network for fine-grained and hierarchical shape segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00972"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Birdal, T., Deng, H., and Tombari, F. (2019, January 15\u201320). 3D point capsule networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00110"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alliegro, A., Boscaini, D., and Tommasi, T. (2021, January 10\u201315). Joint supervised and self-supervised learning for 3d real world challenges. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412483"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1007\/s11263-023-01784-z","article-title":"Multi-modal 3d object detection in autonomous driving: A survey","volume":"131","author":"Wang","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-view convolutional neural networks for 3d shape recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Michel, O., Bar-On, R., Liu, R., Benaim, S., and Hanocka, R. (2022, January 19\u201323). Text2mesh: Text-driven neural stylization for meshes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01313"},{"key":"ref_26","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., and Li, H. (2022, January 18\u201324). Pointclip: Point cloud understanding by clip. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00836"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, T., Dong, B., Yang, Y., Huang, X., Lau, R.W., Ouyang, W., and Zuo, W. (2023, January 2\u20133). Clip2point: Transfer clip to point cloud classification with image-depth pre-training. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.02025"},{"key":"ref_29","unstructured":"Song, W., Zhou, J., Wang, M., Tan, H., Li, N., and Liu, X. (2023). Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model. arXiv."},{"key":"ref_30","first-page":"1","article-title":"Global-to-local generative model for 3d shapes","volume":"37","author":"Wang","year":"2018","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_31","first-page":"1","article-title":"Unsupervised learning for cuboid shape abstraction via joint segmentation from point clouds","volume":"40","author":"Yang","year":"2021","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2980238","article-title":"A scalable active framework for region annotation in 3d shape collections","volume":"35","author":"Yi","year":"2016","journal-title":"Acm Trans. Graph. (ToG)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tchapmi, L.P., Kosaraju, V., Rezatofighi, H., Reid, I., and Savarese, S. (2019, January 15\u201320). Topnet: Structural point cloud decoder. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00047"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xie, H., Yao, H., Zhou, S., Mao, J., Zhang, S., and Sun, W. (2020, January 23\u201328). Grnet: Gridding residual network for dense point cloud completion. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58545-7_21"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, X., Ang, M.H., and Lee, G.H. (2021, January 10\u201317). Voxel-based network for shape completion by leveraging edge generation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01294"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/12\/1588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:31:05Z","timestamp":1760131865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/12\/1588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["e25121588"],"URL":"https:\/\/doi.org\/10.3390\/e25121588","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2023,11,27]]}}}