Point-GSMAE: A graph convolution and scale-based masked autoencoder for 3D point cloud representation
Masked Autoencoders (MAEs) have demonstrated considerable potential in advancing self-supervised learning for 3D point cloud representation. Nevertheless, existing MAE-based approaches, predominantly relying on Transformer architectures, struggle to effectively model interactions between points in l...
Gespeichert in:
| Veröffentlicht in: | Information sciences Jg. 719; S. 122474 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.11.2025
|
| Schlagworte: | |
| ISSN: | 0020-0255 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Masked Autoencoders (MAEs) have demonstrated considerable potential in advancing self-supervised learning for 3D point cloud representation. Nevertheless, existing MAE-based approaches, predominantly relying on Transformer architectures, struggle to effectively model interactions between points in local neighborhoods. This limitation hinders the ability to capture fine-grained local geometric structures of point clouds, negatively impacting tasks that depend on local geometric relationships. In response to this issue, we present Point-GSMAE, an innovative MAE framework for 3D point clouds. This framework integrates graph convolution and graph scale to enhance local geometric modeling. Specifically, it constructs weighted adjacency matrices to encode relationships between neighboring points, edges, and center points, enabling graph convolution to aggregate neighborhood information into center points for precise modeling of local geometric structures. Furthermore, we introduce a graph scale component as a complementary descriptor capturing both the graph structure and spatial distribution, enriching the representation of local geometric properties. To further refine the learned representations, we incorporate a scale consistency loss function, aligning the reconstructed point clouds with their original structures and improving sensitivity to scale variations within local neighborhoods. Comprehensive experiments on multiple datasets demonstrate the efficacy of Point-GSMAE, outperforming existing Transformer-based MAE methods while requiring fewer parameters. |
|---|---|
| AbstractList | Masked Autoencoders (MAEs) have demonstrated considerable potential in advancing self-supervised learning for 3D point cloud representation. Nevertheless, existing MAE-based approaches, predominantly relying on Transformer architectures, struggle to effectively model interactions between points in local neighborhoods. This limitation hinders the ability to capture fine-grained local geometric structures of point clouds, negatively impacting tasks that depend on local geometric relationships. In response to this issue, we present Point-GSMAE, an innovative MAE framework for 3D point clouds. This framework integrates graph convolution and graph scale to enhance local geometric modeling. Specifically, it constructs weighted adjacency matrices to encode relationships between neighboring points, edges, and center points, enabling graph convolution to aggregate neighborhood information into center points for precise modeling of local geometric structures. Furthermore, we introduce a graph scale component as a complementary descriptor capturing both the graph structure and spatial distribution, enriching the representation of local geometric properties. To further refine the learned representations, we incorporate a scale consistency loss function, aligning the reconstructed point clouds with their original structures and improving sensitivity to scale variations within local neighborhoods. Comprehensive experiments on multiple datasets demonstrate the efficacy of Point-GSMAE, outperforming existing Transformer-based MAE methods while requiring fewer parameters. |
| ArticleNumber | 122474 |
| Author | Yang, Chaozhi He, Xiao Li, Guanlin Xiao, Qian Li, Zongmin Bai, Yun |
| Author_xml | – sequence: 1 givenname: Yun surname: Bai fullname: Bai, Yun organization: School of Mathematics and Computer Science, Lishui University, Lishui, 323000, China – sequence: 2 givenname: Chaozhi surname: Yang fullname: Yang, Chaozhi organization: QINGDAO Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong, 266580, China – sequence: 3 givenname: Guanlin orcidid: 0000-0003-0774-2878 surname: Li fullname: Li, Guanlin organization: QINGDAO Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong, 266580, China – sequence: 4 givenname: Xiao surname: He fullname: He, Xiao organization: QINGDAO Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong, 266580, China – sequence: 5 givenname: Qian surname: Xiao fullname: Xiao, Qian organization: QINGDAO Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong, 266580, China – sequence: 6 givenname: Zongmin surname: Li fullname: Li, Zongmin email: lizongmin@upc.edu.cn organization: QINGDAO Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong, 266580, China |
| BookMark | eNp9kMtOwzAQRb0oEi3wAez8Awl-xUlgVZVSkIpAAtaWY0_AJbUjO63E35OorFldaaR7dOcs0MwHDwhdU5JTQuXNLnc-5YywIqeMiVLM0JwQRrLxUpyjRUo7QogopZwjeA3OD9nm7Xm5vsVL_Bl1_4VN8MfQHQYXPNbe4mR0B1mjE1i81-l7DH0YAngTLETchoj5Pe4nFDZdOFgcoY-QwA96glyis1Z3Ca7-8gJ9PKzfV4_Z9mXztFpuM8MKOmSCGcq1FNrqsmk5LYyF2lTEVnVb1awxohatoVRQLiUvrLbclppaIKKSWjb8AtET18SQUoRW9dHtdfxRlKjJjdqp0Y2a3KiTm7Fzd-rAOOzoIKpk3PgZWBfBDMoG90_7F_sHcSs |
| Cites_doi | 10.1016/j.ins.2022.10.135 10.1016/j.inffus.2023.102043 10.1109/TMM.2021.3074240 10.1109/TIP.2014.2311377 10.1016/j.cag.2019.11.005 10.1109/JAS.2023.123432 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Inc. |
| Copyright_xml | – notice: 2025 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2025.122474 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| ExternalDocumentID | 10_1016_j_ins_2025_122474 S0020025525006061 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 77I 8P~ 9JN 9JO AAAKF AAAKG AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AATTM AAXKI AAXUO AAYFN AAYWO ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABUCO ABWVN ABXDB ACDAQ ACGFS ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADGUI ADJOM ADMUD ADNMO ADTZH ADVLN AEBSH AECPX AEIPS AEKER AENEX AEUPX AFFNX AFJKZ AFPUW AFTJW AGHFR AGQPQ AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGII AIGVJ AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APLSM APXCP ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- ~HD 9DU AAYXX CITATION |
| ID | FETCH-LOGICAL-c251t-42c13a64ada7bf315cde9c80d89f892bc494fc114136635dad3d7a1de0486a6b3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001528665100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Sat Nov 29 07:35:33 EST 2025 Sat Oct 11 16:51:56 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Graph scale Point cloud Graph convolution Masked autoencoder |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c251t-42c13a64ada7bf315cde9c80d89f892bc494fc114136635dad3d7a1de0486a6b3 |
| ORCID | 0000-0003-0774-2878 |
| ParticipantIDs | crossref_primary_10_1016_j_ins_2025_122474 elsevier_sciencedirect_doi_10_1016_j_ins_2025_122474 |
| PublicationCentury | 2000 |
| PublicationDate | November 2025 2025-11-00 |
| PublicationDateYYYYMMDD | 2025-11-01 |
| PublicationDate_xml | – month: 11 year: 2025 text: November 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2025 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Wang, Huang, Hou, Zhang, Shan (br0310) 2019 Qi, Dong, Fan, Ge, Zhang, Ma, Yi (br0360) 2023 Yu, Rui, Tao (br0190) 2014; 23 Qi, Su, Mo, Guibas (br0010) 2017 Zhao, Jiang, Jia, Torr, Koltun (br0080) 2021 Qi, Yi, Su, Guibas (br0020) 2017; 30 Chang, Funkhouser, Guibas, Hanrahan, Huang, Li, Savarese, Savva, Song, Su (br0380) 2015 Wang, Sun, Liu, Sarma, Bronstein, Solomon (br0030) 2019; 38 Thomas, Qi, Deschaud, Marcotegui, Goulette, Guibas (br0240) 2019 Wu, Song, Khosla, Yu, Zhang, Tang, Xiao (br0400) 2015 Dong, Qi, Zhang, Zhang, Sun, Ge, Yi, Ma (br0210) 2022 Uy, Pham, Hua, Nguyen, Yeung (br0390) 2019 Qiu, Anwar, Barnes (br0070) 2021; 24 Wen, Long, Yu, Tao (br0060) 2024 Guo, Zhang, Qiu, Li, Heng (br0370) 2023 Zhou, Feng, Fang, Wei, Qin, Lu (br0040) 2021 Liu, Cai, Lee (br0430) 2022 Ma, Qin, You, Ran, Fu (br0420) 2022 Hamdi, Giancola, Ghanem (br0270) 2021 Afham, Dissanayake, Dissanayake, Dharmasiri, Thilakarathna, Rodrigo (br0330) 2022 Liu, Tian, Lv, Li, Wang (br0090) 2023; 11 Xu, Ding, Zhao, Qi (br0250) 2021 Goyal, Law, Liu, Newell, Deng (br0410) 2021 Zha, Ji, Li, Li, Dai, Chen, Wang, Xia (br0100) 2024; vol. 38 Graham, Engelcke, Van Der Maaten (br0260) 2018 Chen, Wang, Yang, Yu, Yuan, Yue (br0440) 2024; 36 Tian, Zheng, Zuo, Zhang, Zhang, Lin (br0170) 2024; 102 Li, Muller, Thabet, Ghanem (br0230) 2019 Armeni, Sener, Zamir, Jiang, Brilakis, Fischer, Savarese (br0450) 2016 Sanghi (br0340) 2020 Xie, Wang, Yu, Anandkumar, Alvarez, Luo (br0150) 2021; 34 Howard, Sandler, Chu, Chen, Chen, Tan, Wang, Zhu, Pang, Vasudevan (br0130) 2019 Wang, Zha, He, Yang, Zhang (br0350) 2024 Redmon, Divvala, Girshick, Farhadi (br0140) 2016 Yu, Tang, Rao, Huang, Zhou, Lu (br0280) 2022 Zhang, Guo, Gao, Fang, Zhao, Wang, Qiao, Li (br0200) 2022; 35 Zhang, Wang, Qiao, Gao, Li (br0220) 2023 Pang, Wang, Tay, Liu, Tian, Yuan (br0120) 2022 Van Nguyen, Lai, Veyseh, Nguyen (br0160) 2021 Te, Hu, Zheng, Guo (br0050) 2018 Zhang, Rabbat (br0290) 2018 Xie, Gu, Guo, Qi, Guibas, Litany (br0320) 2020 Zhang, Zhang, Yan (br0110) 2024 Chen, Song, Su, Zhao, Zhang (br0180) 2022; 616 Lu, Chen, Xie, Luo (br0300) 2020; 86 Yu (10.1016/j.ins.2025.122474_br0280) 2022 Liu (10.1016/j.ins.2025.122474_br0430) 2022 Wang (10.1016/j.ins.2025.122474_br0350) 2024 Graham (10.1016/j.ins.2025.122474_br0260) 2018 Hamdi (10.1016/j.ins.2025.122474_br0270) 2021 Wu (10.1016/j.ins.2025.122474_br0400) 2015 Thomas (10.1016/j.ins.2025.122474_br0240) 2019 Uy (10.1016/j.ins.2025.122474_br0390) 2019 Guo (10.1016/j.ins.2025.122474_br0370) Li (10.1016/j.ins.2025.122474_br0230) 2019 Ma (10.1016/j.ins.2025.122474_br0420) Dong (10.1016/j.ins.2025.122474_br0210) Wang (10.1016/j.ins.2025.122474_br0030) 2019; 38 Liu (10.1016/j.ins.2025.122474_br0090) 2023; 11 Xu (10.1016/j.ins.2025.122474_br0250) 2021 Afham (10.1016/j.ins.2025.122474_br0330) 2022 Goyal (10.1016/j.ins.2025.122474_br0410) 2021 Van Nguyen (10.1016/j.ins.2025.122474_br0160) Zhang (10.1016/j.ins.2025.122474_br0220) 2023 Xie (10.1016/j.ins.2025.122474_br0150) 2021; 34 Te (10.1016/j.ins.2025.122474_br0050) 2018 Sanghi (10.1016/j.ins.2025.122474_br0340) 2020 Qiu (10.1016/j.ins.2025.122474_br0070) 2021; 24 Lu (10.1016/j.ins.2025.122474_br0300) 2020; 86 Zhou (10.1016/j.ins.2025.122474_br0040) 2021 Qi (10.1016/j.ins.2025.122474_br0010) 2017 Zhang (10.1016/j.ins.2025.122474_br0200) 2022; 35 Pang (10.1016/j.ins.2025.122474_br0120) 2022 Chen (10.1016/j.ins.2025.122474_br0180) 2022; 616 Wang (10.1016/j.ins.2025.122474_br0310) 2019 Redmon (10.1016/j.ins.2025.122474_br0140) 2016 Xie (10.1016/j.ins.2025.122474_br0320) 2020 Chang (10.1016/j.ins.2025.122474_br0380) Chen (10.1016/j.ins.2025.122474_br0440) 2024; 36 Armeni (10.1016/j.ins.2025.122474_br0450) 2016 Qi (10.1016/j.ins.2025.122474_br0020) 2017; 30 Zha (10.1016/j.ins.2025.122474_br0100) 2024; vol. 38 Zhang (10.1016/j.ins.2025.122474_br0290) 2018 Howard (10.1016/j.ins.2025.122474_br0130) 2019 Tian (10.1016/j.ins.2025.122474_br0170) 2024; 102 Zhao (10.1016/j.ins.2025.122474_br0080) 2021 Qi (10.1016/j.ins.2025.122474_br0360) 2023 Wen (10.1016/j.ins.2025.122474_br0060) 2024 Yu (10.1016/j.ins.2025.122474_br0190) 2014; 23 Zhang (10.1016/j.ins.2025.122474_br0110) |
| References_xml | – start-page: 1314 year: 2019 end-page: 1324 ident: br0130 article-title: Searching for mobilenetv3 publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – volume: 36 year: 2024 ident: br0440 article-title: Pointgpt: auto-regressively generative pre-training from point clouds publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1912 year: 2015 end-page: 1920 ident: br0400 article-title: 3d shapenets: a deep representation for volumetric shapes publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 10296 year: 2019 end-page: 10305 ident: br0310 article-title: Graph attention convolution for point cloud semantic segmentation publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – start-page: 19313 year: 2022 end-page: 19322 ident: br0280 article-title: Point-bert: pre-training 3d point cloud transformers with masked point modeling publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – year: 2022 ident: br0210 article-title: Autoencoders as cross-modal teachers: can pretrained 2d image transformers help 3d representation learning? – start-page: 1588 year: 2019 end-page: 1597 ident: br0390 article-title: Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – start-page: 746 year: 2018 end-page: 754 ident: br0050 article-title: Rgcnn: regularized graph cnn for point cloud segmentation publication-title: Proceedings of the 26th ACM International Conference on Multimedia – year: 2022 ident: br0420 article-title: Rethinking network design and local geometry in point cloud: a simple residual mlp framework – year: 2024 ident: br0350 article-title: Rethinking masked representation learning for 3d point cloud understanding publication-title: IEEE Trans. Image Process. – year: 2023 ident: br0370 article-title: Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training – start-page: 657 year: 2022 end-page: 675 ident: br0430 article-title: Masked discrimination for self-supervised learning on point clouds publication-title: European Conference on Computer Vision – volume: 35 start-page: 27061 year: 2022 end-page: 27074 ident: br0200 article-title: Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1534 year: 2016 end-page: 1543 ident: br0450 article-title: 3d semantic parsing of large-scale indoor spaces publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 604 year: 2022 end-page: 621 ident: br0120 article-title: Masked autoencoders for point cloud self-supervised learning publication-title: European Conference on Computer Vision – start-page: 779 year: 2016 end-page: 788 ident: br0140 article-title: You only look once: unified, real-time object detection publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 21769 year: 2023 end-page: 21780 ident: br0220 article-title: Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 102 year: 2024 ident: br0170 article-title: A cross transformer for image denoising publication-title: Inf. Fusion – start-page: 652 year: 2017 end-page: 660 ident: br0010 article-title: Pointnet: deep learning on point sets for 3d classification and segmentation publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 574 year: 2020 end-page: 591 ident: br0320 article-title: Pointcontrast: unsupervised pre-training for 3d point cloud understanding publication-title: Computer Vision–ECCV 2020: 16th European Conference – start-page: 3173 year: 2021 end-page: 3182 ident: br0250 article-title: Paconv: position adaptive convolution with dynamic kernel assembling on point clouds publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 616 start-page: 526 year: 2022 end-page: 538 ident: br0180 article-title: Learning user sentiment orientation in social networks for sentiment analysis publication-title: Inf. Sci. – start-page: 6411 year: 2019 end-page: 6420 ident: br0240 article-title: Kpconv: flexible and deformable convolution for point clouds publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – start-page: 4965 year: 2021 end-page: 4974 ident: br0040 article-title: Adaptive graph convolution for point cloud analysis publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – volume: 86 start-page: 42 year: 2020 end-page: 51 ident: br0300 article-title: Pointngcnn: deep convolutional networks on 3d point clouds with neighborhood graph filters publication-title: Comput. Graph. – volume: 30 year: 2017 ident: br0020 article-title: Pointnet++: deep hierarchical feature learning on point sets in a metric space publication-title: Adv. Neural Inf. Process. Syst. – volume: 34 start-page: 12077 year: 2021 end-page: 12090 ident: br0150 article-title: Segformer: simple and efficient design for semantic segmentation with transformers publication-title: Adv. Neural Inf. Process. Syst. – year: 2024 ident: br0110 article-title: Pcp-mae: learning to predict centers for point masked autoencoders – start-page: 9267 year: 2019 end-page: 9276 ident: br0230 article-title: Deepgcns: can gcns go as deep as cnns? publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – year: 2021 ident: br0160 article-title: Trankit: a light-weight transformer-based toolkit for multilingual natural language processing – start-page: 3809 year: 2021 end-page: 3820 ident: br0410 article-title: Revisiting point cloud shape classification with a simple and effective baseline publication-title: International Conference on Machine Learning – volume: 11 start-page: 231 year: 2023 end-page: 239 ident: br0090 article-title: Point cloud classification using content-based transformer via clustering in feature space publication-title: IEEE/CAA J. Autom. Sin. – volume: 23 start-page: 2019 year: 2014 end-page: 2032 ident: br0190 article-title: Click prediction for web image reranking using multimodal sparse coding publication-title: IEEE Trans. Image Process. – start-page: 9902 year: 2022 end-page: 9912 ident: br0330 article-title: Crosspoint: self-supervised cross-modal contrastive learning for 3d point cloud understanding publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – start-page: 6279 year: 2018 end-page: 6283 ident: br0290 article-title: A graph-cnn for 3d point cloud classification publication-title: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – start-page: 28223 year: 2023 end-page: 28243 ident: br0360 article-title: Contrast with reconstruct: contrastive 3d representation learning guided by generative pretraining publication-title: International Conference on Machine Learning – year: 2024 ident: br0060 article-title: Pointwavelet: learning in spectral domain for 3-d point cloud analysis publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 24 start-page: 1943 year: 2021 end-page: 1955 ident: br0070 article-title: Geometric back-projection network for point cloud classification publication-title: IEEE Trans. Multimed. – volume: vol. 38 start-page: 6962 year: 2024 end-page: 6970 ident: br0100 article-title: Towards compact 3d representations via point feature enhancement masked autoencoders publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – volume: 38 start-page: 1 year: 2019 end-page: 12 ident: br0030 article-title: Dynamic graph cnn for learning on point clouds publication-title: ACM Trans. Graph. – start-page: 9224 year: 2018 end-page: 9232 ident: br0260 article-title: 3d semantic segmentation with submanifold sparse convolutional networks publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – year: 2015 ident: br0380 article-title: Shapenet: an information-rich 3d model repository – start-page: 16259 year: 2021 end-page: 16268 ident: br0080 article-title: Point transformer publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – start-page: 626 year: 2020 end-page: 642 ident: br0340 article-title: Info3d: representation learning on 3d objects using mutual information maximization and contrastive learning publication-title: Computer Vision–ECCV 2020: 16th European Conference – start-page: 1 year: 2021 end-page: 11 ident: br0270 article-title: Mvtn: multi-view transformation network for 3d shape recognition publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – volume: 35 start-page: 27061 year: 2022 ident: 10.1016/j.ins.2025.122474_br0200 article-title: Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.ins.2025.122474_br0380 – start-page: 9902 year: 2022 ident: 10.1016/j.ins.2025.122474_br0330 article-title: Crosspoint: self-supervised cross-modal contrastive learning for 3d point cloud understanding – volume: vol. 38 start-page: 6962 year: 2024 ident: 10.1016/j.ins.2025.122474_br0100 article-title: Towards compact 3d representations via point feature enhancement masked autoencoders – start-page: 1314 year: 2019 ident: 10.1016/j.ins.2025.122474_br0130 article-title: Searching for mobilenetv3 – start-page: 9267 year: 2019 ident: 10.1016/j.ins.2025.122474_br0230 article-title: Deepgcns: can gcns go as deep as cnns? – start-page: 1 year: 2021 ident: 10.1016/j.ins.2025.122474_br0270 article-title: Mvtn: multi-view transformation network for 3d shape recognition – start-page: 19313 year: 2022 ident: 10.1016/j.ins.2025.122474_br0280 article-title: Point-bert: pre-training 3d point cloud transformers with masked point modeling – volume: 616 start-page: 526 year: 2022 ident: 10.1016/j.ins.2025.122474_br0180 article-title: Learning user sentiment orientation in social networks for sentiment analysis publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.10.135 – start-page: 21769 year: 2023 ident: 10.1016/j.ins.2025.122474_br0220 article-title: Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders – start-page: 3173 year: 2021 ident: 10.1016/j.ins.2025.122474_br0250 article-title: Paconv: position adaptive convolution with dynamic kernel assembling on point clouds – ident: 10.1016/j.ins.2025.122474_br0160 – volume: 102 year: 2024 ident: 10.1016/j.ins.2025.122474_br0170 article-title: A cross transformer for image denoising publication-title: Inf. Fusion doi: 10.1016/j.inffus.2023.102043 – start-page: 6411 year: 2019 ident: 10.1016/j.ins.2025.122474_br0240 article-title: Kpconv: flexible and deformable convolution for point clouds – volume: 30 year: 2017 ident: 10.1016/j.ins.2025.122474_br0020 article-title: Pointnet++: deep hierarchical feature learning on point sets in a metric space publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.ins.2025.122474_br0210 – start-page: 9224 year: 2018 ident: 10.1016/j.ins.2025.122474_br0260 article-title: 3d semantic segmentation with submanifold sparse convolutional networks – start-page: 1912 year: 2015 ident: 10.1016/j.ins.2025.122474_br0400 article-title: 3d shapenets: a deep representation for volumetric shapes – start-page: 604 year: 2022 ident: 10.1016/j.ins.2025.122474_br0120 article-title: Masked autoencoders for point cloud self-supervised learning – volume: 38 start-page: 1 year: 2019 ident: 10.1016/j.ins.2025.122474_br0030 article-title: Dynamic graph cnn for learning on point clouds publication-title: ACM Trans. Graph. – ident: 10.1016/j.ins.2025.122474_br0370 – start-page: 574 year: 2020 ident: 10.1016/j.ins.2025.122474_br0320 article-title: Pointcontrast: unsupervised pre-training for 3d point cloud understanding – start-page: 3809 year: 2021 ident: 10.1016/j.ins.2025.122474_br0410 article-title: Revisiting point cloud shape classification with a simple and effective baseline – start-page: 657 year: 2022 ident: 10.1016/j.ins.2025.122474_br0430 article-title: Masked discrimination for self-supervised learning on point clouds – start-page: 10296 year: 2019 ident: 10.1016/j.ins.2025.122474_br0310 article-title: Graph attention convolution for point cloud semantic segmentation – ident: 10.1016/j.ins.2025.122474_br0110 – start-page: 626 year: 2020 ident: 10.1016/j.ins.2025.122474_br0340 article-title: Info3d: representation learning on 3d objects using mutual information maximization and contrastive learning – start-page: 1534 year: 2016 ident: 10.1016/j.ins.2025.122474_br0450 article-title: 3d semantic parsing of large-scale indoor spaces – ident: 10.1016/j.ins.2025.122474_br0420 – year: 2024 ident: 10.1016/j.ins.2025.122474_br0060 article-title: Pointwavelet: learning in spectral domain for 3-d point cloud analysis publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 24 start-page: 1943 year: 2021 ident: 10.1016/j.ins.2025.122474_br0070 article-title: Geometric back-projection network for point cloud classification publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2021.3074240 – start-page: 4965 year: 2021 ident: 10.1016/j.ins.2025.122474_br0040 article-title: Adaptive graph convolution for point cloud analysis – start-page: 652 year: 2017 ident: 10.1016/j.ins.2025.122474_br0010 article-title: Pointnet: deep learning on point sets for 3d classification and segmentation – volume: 36 year: 2024 ident: 10.1016/j.ins.2025.122474_br0440 article-title: Pointgpt: auto-regressively generative pre-training from point clouds publication-title: Adv. Neural Inf. Process. Syst. – volume: 34 start-page: 12077 year: 2021 ident: 10.1016/j.ins.2025.122474_br0150 article-title: Segformer: simple and efficient design for semantic segmentation with transformers publication-title: Adv. Neural Inf. Process. Syst. – volume: 23 start-page: 2019 year: 2014 ident: 10.1016/j.ins.2025.122474_br0190 article-title: Click prediction for web image reranking using multimodal sparse coding publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2014.2311377 – start-page: 28223 year: 2023 ident: 10.1016/j.ins.2025.122474_br0360 article-title: Contrast with reconstruct: contrastive 3d representation learning guided by generative pretraining – start-page: 746 year: 2018 ident: 10.1016/j.ins.2025.122474_br0050 article-title: Rgcnn: regularized graph cnn for point cloud segmentation – start-page: 6279 year: 2018 ident: 10.1016/j.ins.2025.122474_br0290 article-title: A graph-cnn for 3d point cloud classification – volume: 86 start-page: 42 year: 2020 ident: 10.1016/j.ins.2025.122474_br0300 article-title: Pointngcnn: deep convolutional networks on 3d point clouds with neighborhood graph filters publication-title: Comput. Graph. doi: 10.1016/j.cag.2019.11.005 – year: 2024 ident: 10.1016/j.ins.2025.122474_br0350 article-title: Rethinking masked representation learning for 3d point cloud understanding publication-title: IEEE Trans. Image Process. – volume: 11 start-page: 231 year: 2023 ident: 10.1016/j.ins.2025.122474_br0090 article-title: Point cloud classification using content-based transformer via clustering in feature space publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2023.123432 – start-page: 779 year: 2016 ident: 10.1016/j.ins.2025.122474_br0140 article-title: You only look once: unified, real-time object detection – start-page: 16259 year: 2021 ident: 10.1016/j.ins.2025.122474_br0080 article-title: Point transformer – start-page: 1588 year: 2019 ident: 10.1016/j.ins.2025.122474_br0390 article-title: Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data |
| SSID | ssj0004766 |
| Score | 2.4763901 |
| Snippet | Masked Autoencoders (MAEs) have demonstrated considerable potential in advancing self-supervised learning for 3D point cloud representation. Nevertheless,... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 122474 |
| SubjectTerms | Graph convolution Graph scale Masked autoencoder Point cloud |
| Title | Point-GSMAE: A graph convolution and scale-based masked autoencoder for 3D point cloud representation |
| URI | https://dx.doi.org/10.1016/j.ins.2025.122474 |
| Volume | 719 |
| WOSCitedRecordID | wos001528665100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0004766 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3dT9RAEN8Q4EEfjKJGVMw8EB8kNW23ZVvfLngKBAmJmBxPzba7DQfaXrieIf71zuxurwtKoia-9C57120z8-t0voex7STlNcIkDLKIo4ESk6MpU2FQJmlZ61DGUpmW-Ufi-DibTPITV3E9N-MERNNk19f57L-yGteQ2VQ6-xfsXm6KC_gdmY5HZDse_4jxJ-206YKPnz-Nxrbs3PSkNunl7romYDBH5uiAXmJq55ucX-KHXHQt9bWk9hKUfchphgNutlN9bRdU5TIbapUaX6t1NU1mb_dKHbzvdt712WKJwjPnot47l-2P8-kyJcj8DyFLvTsGFy0tTqay9d0Tcerq9JY-s75u5kZaJympAVkzvhwWVnb-ItOte-ECDRFqrx6nbykYaEf73GqVTZFnYyQh-EK0zNAqXotFmqPAXhsdjCeHQ8WssFHs_j76eLfJ_Lt1od9rLJ4WcvqQPXDmA4ws2x-xFd1ssPteU8kNtuVKUeA1eHwBJ8QfM-0B5B2MwMADPHgAwgM8eICFB3jwANwX-Hsw8AADD7gJjyfsy4fx6d5-4IZtBBWquB0-plXE5W4ilRRlzaO0UjqvslBleZ3lcVkleVJXaD1HnJRUhc-wEjJSmno2yt2SP2WrTdvoZwxilaqINGv8HRVAUSrNSyWiWMlSyyjbZG96ghYz21Ol6JMNLwqkfkHULyz1N1nSk7xwCLbKXoH4uPu05_922gt2bwDxS7baXS30FluvvnfT-dUrh6Kfim6FXg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Point-GSMAE%3A+A+graph+convolution+and+scale-based+masked+autoencoder+for+3D+point+cloud+representation&rft.jtitle=Information+sciences&rft.au=Bai%2C+Yun&rft.au=Yang%2C+Chaozhi&rft.au=Li%2C+Guanlin&rft.au=He%2C+Xiao&rft.date=2025-11-01&rft.pub=Elsevier+Inc&rft.issn=0020-0255&rft.volume=719&rft_id=info:doi/10.1016%2Fj.ins.2025.122474&rft.externalDocID=S0020025525006061 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |