{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T23:52:56Z","timestamp":1769039576141,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph. Our network provides the required details to analyze, generate and estimate bounding boxes by spatially structuring the input data into graphs. Our method proposes a keypoint attention mechanism that aggregates the relative features between each point to estimate the category and pose of the object to which the vertices of the graph belong, and also designs nine degrees of freedom of multi-object pose estimation. In addition, to avoid gimbal lock in 3D space, we use quaternion rotation, instead of Euler angle. Experimental results showed that memory usage and efficiency could be improved by aggregating point features from the point cloud and their neighbors in a graph structure. Overall, the system achieved comparable performance against state-of-the-art systems.<\/jats:p>","DOI":"10.3390\/s22218166","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"8166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-0881","authenticated-orcid":false,"given":"Tae-Won","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Immersive Content Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]},{"given":"Chi-Seo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Smart Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]},{"given":"In-Seon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Smart Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]},{"given":"Min-Su","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Smart Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6595-6415","authenticated-orcid":false,"given":"Soon-Chul","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Smart Convergence, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]},{"given":"Kye-Dong","family":"Jung","sequence":"additional","affiliation":[{"name":"Ingenium College of Liberal Arts, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Weingarten, J.W., Gruener, G., and Siegwart, R. (October, January 28). A state-of-the-art 3D sensor for robot navigation. Proceedings of the 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), Sendai, Japan.","DOI":"10.1109\/IROS.2004.1389728"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cort\u00e9s Gallardo Medina, E., Velazquez Espitia, V.M., Ch\u00edpuli Silva, D., Fern\u00e1ndez Ruiz de las Cuevas, S., Palacios Hirata, M., Zhu Chen, A., Gonz\u00e1lez Gonz\u00e1lez, J.\u00c1., Bustamante-Bello, R., and Moreno-Garc\u00eda, C.F. (2021). Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems. Appl. Sci., 11.","DOI":"10.20944\/preprints202102.0048.v1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A Survey of Autonomous Driving: Common Practices and Emerging Technologies","volume":"8","author":"Yurtsever","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wolcott, R.W., and Eustice, R.M. (2014, January 14\u201318). Visual localization within LIDAR maps for automated urban driving. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6942558"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, X., Guo, W., Li, M., Chen, C., and Sun, L. (2013, January 26\u201328). Generating colored point cloud under the calibration between TOF and RGB cameras. Proceedings of the 2013 IEEE International Conference on Information and Automation (ICIA), Yingchuan, China.","DOI":"10.1109\/ICInfA.2013.6720347"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qi, X., Liao, R., Jia, J., Fidler, S., and Urtasun, R. (2017, January 22\u201329). 3D graph neural networks for RGBD semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.556"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.isprsjprs.2020.03.007","article-title":"Interactive dense point clouds in a game engine","volume":"163","author":"Virtanen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep learning for 3D point clouds: A survey","volume":"Volume 43","author":"Guo","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_9","first-page":"409","article-title":"Lung cancer detection and classification with 3D convolutional neural network (3D-CNN)","volume":"8","author":"Alakwaa","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201323). Voxelnet: End-to-end learning for point cloud based 3D object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_11","unstructured":"Qi, C.R., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A.O., and Geiger, A. (2017, January 21\u201326). OctNet: Learning deep 3D representations at high resolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref_13","unstructured":"Li, G., Mueller, M., Qian, G., Perez, I.C.D., Abualshour, A., Thabet, A.K., and Ghanem, B. (2021). DeepGCNs: Making GCNs go as deep as CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Landrieu, L., and Simonovsky, M. (2018, January 18\u201323). Large-scale point cloud semantic segmentation with superpoint graphs. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00479"},{"key":"ref_15","unstructured":"Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., and Andreopoulos, Y. (November, January 27). Graph-based object classification for neuromorphic vision sensing. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shi, W., and Rajkumar, R. (2020, January 13\u201319). Point-GNN: Graph neural network for 3D object detection in a point cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"ref_18","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations, ICLR, Toulon, France."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jung, T.-W., Jeong, C.-S., Kwon, S.-C., and Jung, K.-D. (2021). Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation. Appl. Sci., 11.","DOI":"10.3390\/app11199187"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2021","journal-title":"AI Open"},{"key":"ref_21","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain."},{"key":"ref_22","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017, January 4\u20139). Inductive representation learning on large graphs. Proceedings of the Advances in Neural Information Processing Systems 30 NIPS, Long Beach, CA, USA."},{"key":"ref_23","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017, January 24\u201326). Graph attention networks. Proceedings of the ICLR, Toulon, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, C., Zeng, H., Huang, J., Hua, X.S., and Zhang, L. (2020, January 13\u201319). Structure aware single-stage 3D object detection from point cloud. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01189"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., and Jia, J. (2020, January 13\u201319). 3DSSD: Point-based 3D single stage object detector. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 15\u201320). PointPillars: Fast encoders for object detection from point clouds. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yan, Y., Mao, Y., and Li, B. (2018). SECOND: Sparsely embedded convolutional detection. Sensors, 18.","DOI":"10.3390\/s18103337"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zheng, W., Tang, W., Jiang, L., and Fu, C.W. (2021, January 18). SE-SSD: Self-Ensembling Single-Stage Object Detector from Point Cloud. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online.","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Anguelov, D., Flynn, J., and Kosecka, J. (2016, January 21\u201326). 3D Bounding Box Estimation Using Deep Learning and Geometry. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.597"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ahmadyan, A., Zhang, L., Wei, J., Ablavatski, A., Wei, J., and Grundmann, M. (2021, January 18). Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online.","DOI":"10.1109\/CVPR46437.2021.00773"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/0020-0190(77)90070-9","article-title":"The complexity of finding fixed-radius near neighbors","volume":"6","author":"Jon","year":"1977","journal-title":"Inf. Process. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., and Fitzgibbon, A. (2013, January 23\u201328). Scene coordinate regression forests for camera relocalization in RGB-D images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.377"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Ylioinas, J., Kannala, J., and Rahtu, E. (2017, January 22\u201329). Image-based localization using hourglass networks. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.107"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, J., Ma, L., and Hu, X. (June, January 29). Delving deeper into convolutional neural networks for camera relocalization. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989663"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Valada, A., Radwan, N., and Burgard, W. (2018, January 21\u201326). Deep auxiliary learning for visual localization and odometry. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8462979"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1109\/LRA.2018.2869640","article-title":"VLocNet++: Deep multitask learning for semantic visual localization and odometry","volume":"3","author":"Radwan","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Balntas, V., Li, S., and Prisacariu, V. (2018, January 8\u201314). RelocNet: Continuous metric learning relocalisation using neural nets. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_46"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Krull, A., Nowozin, S., Shotton, J., Michel, F., Gumhold, S., and Rother, C. (2017, January 21\u201326). DSAC-differentiable RANSAC for camera localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.267"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:19Z","timestamp":1760144539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,25]]},"references-count":38,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218166"],"URL":"https:\/\/doi.org\/10.3390\/s22218166","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,25]]}}}