{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:59:31Z","timestamp":1768694371433,"version":"3.49.0"},"reference-count":44,"publisher":"Cambridge University Press (CUP)","issue":"9","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>6D pose estimation can perceive an object\u2019s position and orientation in 3D space, playing a critical role in robotic grasping. However, traditional sparse keypoint-based methods generally rely on a limited number of feature points, restricting their performance under occlusion and viewpoint variations. To address this issue, we propose a novel Neighborhood-aware Graph Aggregation Network (NGANet) for precise pose estimation, which combines fully convolutional networks and graph convolutional networks (GCNs) to establish dense correspondences between 2D\u20133D and 3D\u20133D spaces. The <jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0263574725102130_inline1.png\"\/><jats:tex-math>\n$K$\n<\/jats:tex-math><\/jats:alternatives><\/jats:inline-formula>-nearest neighbor algorithm is integrated to build neighborhood relationships within isolated point clouds, followed by GCNs to aggregate local geometric features. When combined with mesh data, both surface details and topological shapes can be modeled. A positional encoding attention mechanism is introduced to adaptively fuse these multimodal features into a unified, spatially coherent representation about pose-specific features. Extensive experiments indicate that our proposed NGANet achieves a higher estimation accuracy on LINEMOD and Occlusion-LINEMOD datasets. In addition, its effectiveness is also validated under real-world scenarios.<\/jats:p>","DOI":"10.1017\/s0263574725102130","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T11:01:26Z","timestamp":1755601286000},"page":"3095-3111","source":"Crossref","is-referenced-by-count":1,"title":["NGANet: Neighborhood-aware Graph Aggregation Network for 6D pose estimation in robotic grasping"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2184-8268","authenticated-orcid":false,"given":"Lu","family":"Chen","sequence":"first","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3724-6066","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"S0263574725102130_ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3446841"},{"key":"S0263574725102130_ref43","doi-asserted-by":"crossref","unstructured":"[43] Li, Z. , Wang, G. and Ji, X. , \u201cDPN: Coordinates-based Disentangled Pose Network for Real-time RGB-based 6-DoF Object Pose 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6728\u20136738.","DOI":"10.1109\/CVPR52688.2022.00662"},{"key":"S0263574725102130_ref3","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574723001285"},{"key":"S0263574725102130_ref15","doi-asserted-by":"crossref","unstructured":"[15] Li, Y. , Wang, G. , Ji, X. , Xiang, Y. and Fox, D. , \u201cDeepIM: Deep Iterative Matching for 6D Pose Estimation,\u201d In: Proceedings of the European Conference on Computer Vision, Springer (2018) pp.657\u2013678.","DOI":"10.1007\/s11263-019-01250-9"},{"key":"S0263574725102130_ref24","doi-asserted-by":"crossref","unstructured":"[24] He, Y. , Huang, H. , Fan, H. , Chen, Q. and Sun, J. , \u201cFFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation,\u201d In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2021) pp. 3002\u20133012.","DOI":"10.1109\/CVPR46437.2021.00302"},{"key":"S0263574725102130_ref34","first-page":"165","volume-title":"The Top Ten Algorithms in Data 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N. , Kaiser, L. and Polosukhin, I. , \u201cAttention is All You Need,\u201d In: Proceedings of the 31st International Conference on Neural Information Processing Systems, MIT (2017) pp. 6000\u20136010."},{"key":"S0263574725102130_ref10","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"S0263574725102130_ref44","doi-asserted-by":"crossref","unstructured":"[44] Di, Y. , Manhardt, F. , Wang, G. , Ji, X. , Navab, N. and Tombari, F. , \u201cSO-Pose: Exploiting Self-occlusion for Direct 6D Pose Estimation,\u201d In: 2021 IEEE\/CVF International Conference on Computer Vision, IEEE Computer Society (2021) pp. 12376\u201312385.","DOI":"10.1109\/ICCV48922.2021.01217"},{"key":"S0263574725102130_ref23","doi-asserted-by":"crossref","unstructured":"[23] He, Y. , Sun, W. , Huang, H. , Liu, J. , Fan, H. and Sun, J. , \u201cPVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation,\u201d In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2020) pp. 11629\u201311638.","DOI":"10.1109\/CVPR42600.2020.01165"},{"key":"S0263574725102130_ref20","doi-asserted-by":"crossref","unstructured":"[20] Lian, R. and Ling, H. , \u201cCheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network,\u201d In: 2023 IEEE\/CVF International Conference on Computer Vision, IEEE Computer Society (2023) pp. 13976\u201313987.","DOI":"10.1109\/ICCV51070.2023.01289"},{"key":"S0263574725102130_ref21","doi-asserted-by":"crossref","unstructured":"[21] Wang, C. , Xu, D. , Zhu, Y. , Mart\u00edn-Mart\u00edn, R. , Lu, C. , Fei-Fei, L. and Savarese, S. , \u201cDenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion,\u201d In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2019) pp. 3338\u20133347.","DOI":"10.1109\/CVPR.2019.00346"},{"key":"S0263574725102130_ref38","unstructured":"[38] Cignoni, P. , Callieri, M. , Corsini, M. , Dellepiane, M. , Ganovelli, F. , Ranzuglia, G. , \u201cMeshLab: An Open-Source Mesh Processing Tool,\u201d In: Eurographics Italian Chapter Conference, Eurographics Association (2008) pp. 129\u2013136."},{"key":"S0263574725102130_ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.09.001"},{"key":"S0263574725102130_ref18","doi-asserted-by":"crossref","unstructured":"[18] Song, C. , Song, J. and Huang, Q. , \u201cHybridPose: 6D Object Pose Estimation Under Hybrid Representations,\u201d In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2020) pp. 428\u2013437.","DOI":"10.1109\/CVPR42600.2020.00051"},{"key":"S0263574725102130_ref36","doi-asserted-by":"crossref","unstructured":"[36] Fey, M. , Lenssen, J. E. , Weichert, F. and M\u00fcller, H. , \u201cSplineCNN: Fast Geometric Deep Learning with continuous B-spline Kernels,\u201d In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2018) pp. 869\u2013877.","DOI":"10.1109\/CVPR.2018.00097"},{"key":"S0263574725102130_ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3318070"},{"key":"S0263574725102130_ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3520437"},{"key":"S0263574725102130_ref27","doi-asserted-by":"crossref","unstructured":"[27] Sarlin, P.-E. , DeTone, D. , Malisiewicz, T. and Rabinovich, A. , \u201cSuperGlue: Learning Feature Matching with Graph Neural Networks,\u201d In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2020) pp. 4937\u20134946.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"S0263574725102130_ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0152-6"},{"key":"S0263574725102130_ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3421790"},{"key":"S0263574725102130_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2021.3134078"},{"key":"S0263574725102130_ref40","doi-asserted-by":"crossref","unstructured":"[40] Brachmann, E. , Michel, F. , Krull, A. , Yang, M. Y. , Gumhold, S. and Rother, C. , \u201cUncertainty-driven 6D pose Estimation of Objects and Scenes from a Single RGB Image,\u201d In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society (2016) pp. 3364\u20133372.","DOI":"10.1109\/CVPR.2016.366"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574725102130","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T09:03:23Z","timestamp":1759914203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574725102130\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":44,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S0263574725102130"],"URL":"https:\/\/doi.org\/10.1017\/s0263574725102130","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,19]]}}}