{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:24:57Z","timestamp":1790839497811,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,15]],"date-time":"2018-08-15T00:00:00Z","timestamp":1534291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["DPI2015-68442-R"],"award-info":[{"award-number":["DPI2015-68442-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method.<\/jats:p>","DOI":"10.3390\/s18082678","type":"journal-article","created":{"date-parts":[[2018,8,15]],"date-time":"2018-08-15T10:40:07Z","timestamp":1534329607000},"page":"2678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8287-688X","authenticated-orcid":false,"given":"Joel","family":"Vidal","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"},{"name":"Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chyi-Yeu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"},{"name":"Taiwan Building Technology Center, National Taiwan University of Science and Technology, Taipei 106, Taiwan"},{"name":"Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xavier","family":"Llad\u00f3","sequence":"additional","affiliation":[{"name":"Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-2710","authenticated-orcid":false,"given":"Robert","family":"Mart\u00ed","sequence":"additional","affiliation":[{"name":"Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.cviu.2013.04.005","article-title":"50 Years of object recognition: Directions forward","volume":"117","author":"Andreopoulos","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1101\/SQB.1990.055.01.083","article-title":"Three-dimensional object recognition","volume":"55","author":"Ullman","year":"1990","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/0004-3702(77)90006-6","article-title":"Description and recognition of curved objects","volume":"8","author":"Nevatia","year":"1977","journal-title":"Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/4078.4081","article-title":"Three-dimensional Object Recognition","volume":"17","author":"Besl","year":"1985","journal-title":"ACM Comput. Surv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.3390\/s90100568","article-title":"State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation","volume":"9","author":"Sansoni","year":"2009","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TPAMI.2014.2316828","article-title":"3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey","volume":"36","author":"Guo","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1109\/PROC.1984.13073","article-title":"Extended Gaussian images","volume":"72","author":"Horn","year":"1984","journal-title":"Proc. IEEE"},{"key":"ref_8","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 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651280"},{"key":"ref_9","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 2011 IEEE International Conference on Robotics and Biomimetics, Phuket, Thailand.","DOI":"10.1109\/ROBIO.2011.6181760"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aldoma, A., Vincze, M., Blodow, N., Gossow, D., Gedikli, S., Rusu, R.B., and Bradski, G. (2011, January 6\u201313). CAD-model recognition and 6DOF pose estimation using 3D cues. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130296"},{"key":"ref_11","first-page":"345","article-title":"Occlusion, clutter, and illumination invariant object recognition","volume":"34","author":"Steger","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Fua, P., and Navab, N. (2010, January 13\u201318). Dominant orientation templates for real-time detection of texture-less objects. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539908"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., and Lepetit, V. (2011, January 6\u201313). Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126326"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., Zabulis, X., Lourakis, M., Obdr\u017e\u00e1lek, \u0160., and Matas, J. (October, January 28). Detection and fine 3D pose estimation of texture-less objects in RGB-D images. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7354005"},{"key":"ref_15","first-page":"249","article-title":"Supervised Machine Learning: A Review of Classification Techniques","volume":"31","author":"Kotsiantis","year":"2007","journal-title":"Informatica"},{"key":"ref_16","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Learning 6D Object Pose Estimation Using 3D Object Coordinates. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Michel, F., Krull, A., Yang, M.Y., Gumhold, S., and Rother, C. (2016, January 27\u201330). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.366"},{"key":"ref_19","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Latent-Class Hough Forests for 3D Object Detection and Pose Estimation. Computer Vision\u2014ECCV 2014, Springer International Publishing."},{"key":"ref_20","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Deep Learning of Local RGB-D Patches for 3DObject Detection and 6D Pose Estimation. Computer Vision\u2014ECCV 2016, Springer International Publishing."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010, January 13\u201318). Model globally, match locally: Efficient and robust 3D object recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wahl, E., Hillenbrand, U., and Hirzinger, G. (2003, January 6\u201310). Surflet-pair-relation histograms: A statistical 3D-shape representation for rapid classification. Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling, Banff, AB, Canada.","DOI":"10.1109\/IM.2003.1240284"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, E., and Medioni, G. (2011, January 25\u201330). 3D object recognition in range images using visibility context. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048203"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Drost, B., and Ilic, S. (2012, January 13\u201315). 3D Object Detection and Localization Using Multimodal Point Pair Features. Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.53"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Birdal, T., and Ilic, S. (2015, January 19\u201322). Point Pair Features Based Object Detection and Pose Estimation Revisited. Proceedings of the 2015 International Conference on 3D Vision, Lyon, France.","DOI":"10.1109\/3DV.2015.65"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Going Further with Point Pair Features. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cviu.2017.09.004","article-title":"A performance evaluation of point pair features","volume":"166","author":"Kiforenko","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vidal, J., Lin, C., and Mart\u00ed, R. (2018, January 20\u201323). 6D pose estimation using an improved method based on point pair features. Proceedings of the 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand.","DOI":"10.1109\/ICCAR.2018.8384709"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hodan, T., Michel, F., Brachmann, E., Kehl, W., Buch, A., Kraft, D., Drost, B., Vidal, J., Ihrke, S., and Zabulis, X. (2018). BOP: Benchmark for 6D Object Pose Estimation. ECCV, in press.","DOI":"10.1007\/978-3-030-01249-6_2"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.mechmachtheory.2015.03.004","article-title":"Euler\u2013Rodrigues formula variations, quaternion conjugation and intrinsic connections","volume":"92","author":"Dai","year":"2015","journal-title":"Mech. Mach. Theory"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10851-009-0161-2","article-title":"Metrics for 3D Rotations: Comparison and Analysis","volume":"35","author":"Huynh","year":"2009","journal-title":"J. Math. Imag. Vis."},{"key":"ref_32","unstructured":"Low, K.L. (2004). Linear Least-Squares Optimization for Point-to-Plane icp Surface Registration, University of North Carolina. Technical Report TR04-004."},{"key":"ref_33","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_34","unstructured":"Hua, G., and J\u00e9gou, H. (2016). On Evaluation of 6D Object Pose Estimation. Computer Vision\u2014ECCV 2016 Workshops, Springer International Publishing."},{"key":"ref_35","unstructured":"(2018, June 07). MVTec HALCON. Available online: https:\/\/www.mvtec.com\/halcon\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Buch, A.G., Kiforenko, L., and Kraft, D. (2017, January 22\u201329). Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.443"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1186\/s40064-016-1906-1","article-title":"Local shape feature fusion for improved matching, pose estimation and 3D object recognition","volume":"5","author":"Buch","year":"2016","journal-title":"SpringerPlus"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:18:49Z","timestamp":1760195929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,15]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["s18082678"],"URL":"https:\/\/doi.org\/10.3390\/s18082678","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,15]]}}}