{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T03:03:53Z","timestamp":1765422233217,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"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>3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.<\/jats:p>","DOI":"10.3390\/s21030910","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T09:25:22Z","timestamp":1611912322000},"page":"910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1167-8322","authenticated-orcid":false,"given":"Cristian","family":"Vilar","sequence":"first","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0282-5471","authenticated-orcid":false,"given":"Silvia","family":"Krug","sequence":"additional","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden"},{"name":"System Design Department, IMMS Institut f\u00fcr Mikroelektronik- und Mechatronik-Systeme Gemeinn\u00fctzige GmbH (IMMS GmbH), Ehrenbergstra\u00dfe 27, 98693 Ilmenau, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8607-4083","authenticated-orcid":false,"given":"Mattias","family":"O\u2019Nils","sequence":"additional","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s10044-019-00804-4","article-title":"3D object recognition and classification: A systematic literature review","volume":"22","author":"Carvalho","year":"2019","journal-title":"Pattern Anal. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1177\/0278364911436019","article-title":"Rigid 3D geometry matching for grasping of known objects in cluttered scenes","volume":"31","author":"Papazov","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., and Davison, A. (2011, January 16\u201319). KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, Santa Barbara, CA, USA.","DOI":"10.1145\/2047196.2047270"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aleman, J., Monjardin Hernandez, H.S., Orozco-Rosas, U., and Picos, K. (2020). Autonomous navigation for a holonomic drive robot in an unknown environment using a depth camera. Optics and Photonics for Information Processing XIV, International Society for Optics and Photonics.","DOI":"10.1117\/12.2568163"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cag.2017.10.007","article-title":"Toward real-time 3D object recognition: A lightweight volumetric CNN framework using multitask learning","volume":"71","author":"Zhi","year":"2018","journal-title":"Comput. Graph. (Pergamon)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161958","DOI":"10.1109\/ACCESS.2020.3021455","article-title":"A Voxelized Fractal Descriptor for 3D Object Recognition","volume":"8","author":"Domenech","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","unstructured":"Wu, Z., and Song, S. (2015, January 7\u201312). 3D ShapeNets: A Deep Representation for Volumetric Shapes. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vilar, C., Krug, S., and Thornberg, B. (2020). Processing chain for 3D histogram of gradients based real-time object recognition. Int. J. Adv. Robot. Syst., 13.","DOI":"10.1177\/1729881420978363"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, S., Yu, H., and Yang, T. (2020). A cylindrical shape descriptor for registration of unstructured point clouds from real-time 3D sensors. J. Real Time Image Process., 1\u20139.","DOI":"10.1007\/s11554-020-01033-3"},{"key":"ref_10","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 19\u201325). PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA."},{"key":"ref_11","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Simon, M., Amende, K., Kraus, A., Honer, J., Samann, T., Kaulbersch, H., Milz, S., and Gross, H.M. (2019, January 18\u201320). Complexer-YOLO: Real-time 3D object detection and tracking on semantic point clouds. Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00158"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/978-3-030-20873-8_44","article-title":"SPNet: Deep 3D Object Classification and Retrieval Using Stereographic Projection","volume":"11365","author":"Yavartanoo","year":"2019","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bayramoglu, N., and Alatan, A.A. (2010, January 23\u201326). Shape index SIFT: Range image recognition using local features. Proceedings of the International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.95"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1109\/ACCESS.2017.2658681","article-title":"3D Object Recognition in Cluttered Scenes With Robust Shape Description and Correspondence Selection","volume":"5","author":"Tang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","article-title":"SHOT: Unique signatures of histograms for surface and texture description q","volume":"125","author":"Salti","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TCSVT.2018.2813083","article-title":"Aligning 2.5D Scene Fragments With Distinctive Local Geometric Features","volume":"29","author":"Yang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tao, W., Hua, X., Yu, K., Chen, X., and Zhao, B. (2020). A Pipeline for 3-D Object Recognition Based on Local Shape Description in Cluttered Scenes. IEEE Trans. Geosci. Remote. Sens., 1\u201316.","DOI":"10.1109\/TGRS.2020.2998683"},{"key":"ref_19","unstructured":"Do Monte Lima, J.P.S., and Teichrieb, V. (2016, January 4\u20137). An efficient global point cloud descriptor for object recognition and pose estimation. Proceedings of the 29th Conference on Graphics, Patterns and Images (SIBGRAPI), Sao Paulo, Brazil."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Aldoma, A., Tombari, F., Di Stefano, L., and Vincze, M. (2012, January 7\u201313). A global hypotheses verification method for 3D object recognition. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33712-3_37"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, H., Liu, N., Wang, X., and Xu, J. (2020). 3D Object Recognition and Pose Estimation from Point Cloud Using Stably Observed Point Pair Feature. IEEE Access, 8.","DOI":"10.1109\/ACCESS.2020.2978255"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/34.765655","article-title":"Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes","volume":"21","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Bradski, G., Thibaux, R., and Hsu, J. (2010, January 18\u201322). Fast 3D recognition and pose using the viewpoint feature histogram. Proceedings of the IEEE\/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651280"},{"key":"ref_24","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (June, January 30). Fast Point Feature Histograms (FPFH) for 3D registration. Proceedings of the International Conference on Robotics and Automation (ICRA), Xi\u2019an, China."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wohlkinger, W., and Vincze, M. (2011, January 7\u201311). Ensemble of shape functions for 3D object classification. Proceedings of the International Conference on Robotics and Biomimetics (ROBIO), Karon Beach, Thailand.","DOI":"10.1109\/ROBIO.2011.6181760"},{"key":"ref_26","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dupre, R., and Argyriou, V. (2015, January 21\u201324). 3D Voxel HOG and Risk Estimation. Proceedings of the International Conference on Digital Signal Processing (DSP), Singapore.","DOI":"10.1109\/ICDSP.2015.7251919"},{"key":"ref_28","unstructured":"Scherer, M., Walter, M., and Schreck, T. (2010, January 1\u20134). Histograms of oriented gradients for 3d object retrieval. Proceedings of the 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Plzen, Czech Republic."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Buch, N., Orwell, J., and Velastin, S.A. (2009, January 7\u201310). 3D extended histogram of oriented gradients (3DHOG) for classification of road users in urban scenes. Proceedings of the British Machine Vision Conference (BMVC), London, UK.","DOI":"10.5244\/C.23.15"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vilar, C., Th\u00f6rnberg, B., and Krug, S. (2019, January 3\u20135). Evaluation of Embedded Camera Systems for Autonomous Wheelchairs. Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), Crete, Greece.","DOI":"10.5220\/0007678700002179"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1006\/cviu.1999.0832","article-title":"MLESAC: A new robust estimator with application to estimating image geometry","volume":"78","author":"Torr","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vilar, C., Krug, S., and Thornberg, B. (2019, January 1\u20133). Rotational Invariant Object Recognition for Robotic Vision. Proceedings of the 3rd International Conference on Automation, Control and Robots (ICACR), Shanghai, China.","DOI":"10.1145\/3365265.3365273"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:08Z","timestamp":1760159828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030910"],"URL":"https:\/\/doi.org\/10.3390\/s21030910","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}