{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:59:29Z","timestamp":1790805569076,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T00:00:00Z","timestamp":1630281600000},"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>Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10\u201330\u00d7) the efficiency of the original SAC-IA method without sacrificing accuracy.<\/jats:p>","DOI":"10.3390\/s21175850","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Efficient 3D Object Recognition from Cluttered Point Cloud"],"prefix":"10.3390","volume":"21","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtai","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechanical of Engineering and Automation, Northeastern University, Shenyang 110167, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/TPAMI.2005.188","article-title":"A performance evaluation of local descriptors","volume":"27","author":"Mikolajczyk","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.patrec.2019.08.016","article-title":"3d object recognition and pose estimation for random bin-picking using partition viewpoint feature histograms","volume":"128","author":"Li","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, F., Liang, C., Ru, C., and Cheng, H. (2019). An improved point cloud descriptor for vision based robotic grasping system. Sensor, 19.","DOI":"10.3390\/s19102225"},{"key":"ref_4","first-page":"1191","article-title":"Cad-based recognition of 3d objects in monocular images","volume":"9","author":"Ulrich","year":"2009","journal-title":"ICRA"},{"key":"ref_5","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, Karon Beach, Thailand.","DOI":"10.1109\/ROBIO.2011.6181760"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Holzbach, A., Beetz, M., and Bradski, G. (October, January 27). Detecting and segmenting objects for mobile manipulation. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshop, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457718"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liang, X., and Cheng, H. (August, January 29). RGB-D Camera based 3D Object Pose Estimation and Grasping. Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, Suzhou, China.","DOI":"10.1109\/CYBER46603.2019.9066550"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aldoma, A., Tombari, F., Rusu, R.B., and Vincze, M. (2012). Our-cvfh\u2013oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6DOF pose estimation. Joint DAGM and OAGM Symposium, Springer.","DOI":"10.1007\/978-3-642-32717-9_12"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Besl, P.J., and McKay, N.D. (1991, January 14\u201315). Method for registration of 3-D shapes. Proceedings of the Robotics \u201991, Boston, MA, USA.","DOI":"10.1109\/34.121791"},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11263-013-0627-y","article-title":"Rotational projection statistics for 3D local surface description and object recognition","volume":"105","author":"Guo","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast point feature histograms (FPFH) for 3D registration. Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TSP.2002.806551","article-title":"Fast algorithm for robust template matching with M-estimators","volume":"51","author":"Chen","year":"2003","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00127126","article-title":"Robust regression methods for computer vision: A review","volume":"6","author":"Meer","year":"1991","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"012037","DOI":"10.1088\/1742-6596\/1004\/1\/012037","article-title":"MLESAC Based Localization of Needle Insertion Using 2D Ultrasound Images","volume":"1004","author":"Xu","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1023\/A:1020224303087","article-title":"Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting","volume":"50","author":"Torr","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.imavis.2004.02.009","article-title":"Randomized RANSAC with T(d,d) test","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/TPAMI.2007.70787","article-title":"Optimal Randomized RANSAC","volume":"30","author":"Chum","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","first-page":"1727","article-title":"Randomized RANSAC with sequential probability ratio test","volume":"2","author":"Matas","year":"2005","journal-title":"IEEE Int. Conf. Comput. Vis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TPAMI.2012.257","article-title":"USAC: A Universal Framework for Random Sample Consensus","volume":"35","author":"Raguram","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Chum, O., and Matas, J. (2005, January 20\u201325). Matching with PROSAC\u2014Progressive sample consensus. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_26","unstructured":"Myatt, D.R., Torr, P., Nasuto, S.J., Bishop, J.M., and Craddock, R. (2021, August 27). NAPSAC: High Noise, High Dimensional Model Parameterisation\u2014It\u2019s in the Bag. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.371.8748&rep=rep1&type=pdf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kai, N., Jin, H., and Dellaert, F. (October, January 29). GroupSAC: Efficient consensus in the presence of groupings. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459241"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chum, O., Ji\u00ed, M., and Kittler, J. (2003). Locally Optimized RANSAC. Joint Pattern Recognition Symposium, Springer.","DOI":"10.1007\/978-3-540-45243-0_31"},{"key":"ref_29","first-page":"21","article-title":"Optimal RANSAC\u2014Towards a Repeatable Algorithm for Finding the Optimal Set","volume":"21","author":"Hast","year":"2015","journal-title":"J. WSCG"},{"key":"ref_30","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.267"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Brachmann, E., and Rother, C. (November, January 27). Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00442"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Steder, B., Rusu, R.B., Konolige, K., and Burgard, W. (2011, January 9\u201313). Point feature extraction on 3D range scans taking into account object boundaries. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980187"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhong, Y. (October, January 27). Intrinsic shape signatures: A shape descriptor for 3d object recognition. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457637"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11263-015-0824-y","article-title":"A comprehensive performance evaluation of 3D local feature descriptors","volume":"116","author":"Guo","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Prakhya, S.M., Liu, B., and Lin, W. (October, January 28). B-SHOT: A binary feature descriptor for fast and efficient keypoint matching on 3D point clouds. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353630"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5850\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:00Z","timestamp":1760165760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,30]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175850"],"URL":"https:\/\/doi.org\/10.3390\/s21175850","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,30]]}}}