{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:51:37Z","timestamp":1776833497934,"version":"3.51.2"},"reference-count":69,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T00:00:00Z","timestamp":1574121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KEY RESEARCH AND DEVELOPMENT PROJECT IN NINGXIA HUI NATIONALITY AUTONOMOUS REGION","award":["2017BY067"],"award-info":[{"award-number":["2017BY067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention.<\/jats:p>","DOI":"10.3390\/s19225046","type":"journal-article","created":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T11:30:17Z","timestamp":1574163017000},"page":"5046","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3333-5718","authenticated-orcid":false,"given":"Lvwen","family":"Huang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinqin","family":"Rao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixia","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuqin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shicheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyun","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Computer Science, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Western E-commerce Co., Ltd., Yinchuan 750004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1186\/2193-1801-3-225","article-title":"Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns","volume":"3","author":"Salau","year":"2014","journal-title":"SpringerPlus"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pezzuolo, A., Guarino, M., Sartori, L., and Marinello, F. (2018). A Feasibility study on the use of a structured light depth-camera for three-dimensional body measurements of dairy cows in free-stall barns. Sensors, 18.","DOI":"10.3390\/s18020673"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.compag.2017.04.014","article-title":"LSSA_CAU: An interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows or pigs","volume":"138","author":"Guo","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compag.2018.03.003","article-title":"On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera","volume":"148","author":"Pezzuolo","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.3168\/jds.S0022-0302(97)76142-3","article-title":"Estimation of body weight from body size measurements and body condition scores in dairy cows","volume":"80","author":"Enevoldsen","year":"1997","journal-title":"J. Dairy Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/0168-1699(96)00003-8","article-title":"Determination of live weight of pigs from dimensions measured using image analysis","volume":"15","author":"Brandl","year":"1996","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.3168\/jds.S0022-0302(97)76277-5","article-title":"Body measurements and body weights of special-fed Holstein veal calves","volume":"80","author":"Wilson","year":"1997","journal-title":"J. Dairy Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.4081\/ijas.2013.e18","article-title":"Body measures and milk production, milk fat globules granulometry and milk fatty acid content in Cabannina cattle breed","volume":"12","author":"Communod","year":"2013","journal-title":"Ital. J. Anim. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, L., Li, S., Zhu, A., Fan, X., Zhang, C., and Wang, H. (2018). Non-contact body measurement for qinchuan cattle with LiDAR sensor. Sensors, 18.","DOI":"10.3390\/s18093014"},{"key":"ref_10","first-page":"1847","article-title":"Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging","volume":"95","author":"McPhee","year":"2017","journal-title":"J. Anim. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rizaldy, A., Persello, C., Gevaert, C., Elberink, S.O., and Vosselman, G. (2018). Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks. Remote Sens., 10.","DOI":"10.3390\/rs10111723"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/LGRS.2018.2868378","article-title":"LiDAR Data Classification Using Spatial Transformation and CNN","volume":"16","author":"He","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/LGRS.2018.2867736","article-title":"Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks","volume":"16","author":"Maltezos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.3390\/rs3112494","article-title":"Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements","volume":"3","author":"Edson","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Maki, N., Nakamura, S., Takano, S., and Okada, Y. (2018, January 4\u20136). 3D Model Generation of Cattle Using Multiple Depth-Maps for ICT Agriculture. Proceedings of the Conference on Complex, Intelligent, and Software Intensive Systems, Matsue, Japan.","DOI":"10.1007\/978-3-319-61566-0_72"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10015-017-0373-2","article-title":"Black cattle body shape and temperature measurement using thermography and KINECT sensor","volume":"22","author":"Kawasue","year":"2017","journal-title":"Artif. Life Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1093\/jas\/sky418","article-title":"A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision","volume":"97","author":"Fernandes","year":"2019","journal-title":"J. Anim. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.compag.2014.01.018","article-title":"A low-cost stereovision system to estimate size and weight of live sheep","volume":"103","author":"Menesatti","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.compag.2018.03.018","article-title":"A portable and automatic Xtion-based measurement system for pig body size","volume":"148","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.compag.2018.08.006","article-title":"Estimating pig weights from images without constraint on posture and illumination","volume":"153","author":"Jun","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.3168\/jds.2010-3467","article-title":"Objective estimation of body condition score by modeling cow body shape from digital images","volume":"94","author":"Azzaro","year":"2011","journal-title":"J. Dairy Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TIFS.2016.2601065","article-title":"Effective and Efficient Global Context Verification for Image Copy Detection","volume":"12","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/TCSVT.2017.2653187","article-title":"Toward Always-On Mobile Object Detection: Energy Versus Performance Tradeoffs for Embedded HOG Feature Extraction","volume":"28","author":"Young","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3792805","DOI":"10.1155\/2017\/3792805","article-title":"Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features","volume":"2017","author":"Zhou","year":"2017","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_26","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J., Wu, Z., Song, S., and Khosla, A. (2015, January 7\u201312). 3D ShapeNets: A deep representation for volumetric shapes. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Boston, MA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1080\/2150704X.2015.1088668","article-title":"Deep learning-based tree classification using mobile LiDAR data","volume":"6","author":"Guan","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nahhas, F.H., Shafri, H.Z.M., Sameen, M.I., Pradhan, B., and Mansor, S. (2018). Deep Learning Approach for Building Detection Using LiDAR-Orthophoto Fusion. J. Sens., 7.","DOI":"10.1155\/2018\/7212307"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"866","DOI":"10.3389\/fpls.2018.00866","article-title":"Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms","volume":"22","author":"Jin","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Hao, S., Mo, K., 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 & Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_31","unstructured":"Qi, C.R., Li, Y., Hao, S., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Klokov, R., and Lempitsky, V. (2017, January 22\u201329). Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.99"},{"key":"ref_33","unstructured":"Zeiler, M.D., and Fergus, R. (2013, January 1\u20138). Visualizing and Understanding Convolutional Networks. Proceedings of the European Conference on Computer Vision, Sydney, Australia."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zeng, W., and Gevers, T. (2018, January 8\u201314). 3D ContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues. Proceedings of the European Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-11015-4_24"},{"key":"ref_35","unstructured":"Marinello, F., Pezzuolo, A., Cillis, D., Gasparini, F., and Sartori, L. (2015, January 15\u201318). Application of Kinect-Sensor for three-dimensional body measurements of cows. Proceedings of the 7th European Precision Livestock Farming, ECPLF 2015. European Conference on Precision Livestock Farming, Milan, Italy."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_37","unstructured":"Tan, C., Sun, F., Tao, K., Zhang, W., Chao, Y., and Liu, C. (2018, January 5\u20137). A Survey on Deep Transfer Learning. Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"14662","DOI":"10.3390\/s131114662","article-title":"Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor","volume":"13","author":"Andujar","year":"2013","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4594","DOI":"10.1109\/TGRS.2018.2829625","article-title":"A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Silpa-Anan, C., and Hartley, R. (2008). Optimised KD-trees for fast image descriptor matching. 2018 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society.","DOI":"10.1109\/CVPR.2008.4587638"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Duarte, D., Nex, F., Kerle, N., and Vosselman, G. (2018). Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10101636"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2018.11.002","article-title":"Kafnets: Kernel-based non-parametric activation functions for neural networks","volume":"110","author":"Scardapane","year":"2019","journal-title":"Neural Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neunet.2018.11.005","article-title":"A comparison of deep networks with ReLU activation function and linear spline-type methods","volume":"110","author":"Eckle","year":"2019","journal-title":"Neural Netw."},{"key":"ref_44","unstructured":"(2019, October 10). ShapeNet Datasource. Available online: https:\/\/shapenet.cs.stanford.edu\/ericyi\/shapenetcore_partanno_segmentation_benchmark_v0.zip."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/72.750573","article-title":"A method to determine the required number of neural-network training repetitions","volume":"10","author":"Iyer","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neunet.2018.01.016","article-title":"Effective neural network training with adaptive learning rate based on training loss","volume":"101","author":"Takase","year":"2018","journal-title":"Neural Netw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1109\/JSEN.2010.2101060","article-title":"Lock-in Time-of-Flight (ToF) Cameras: A Survey","volume":"11","author":"Foix","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.measurement.2018.10.013","article-title":"Point cloud filtering on UAV based point cloud","volume":"133","author":"Zeybek","year":"2019","journal-title":"Measurement"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/9.847749","article-title":"A nonlinear filtering algorithm based on an approximation of the conditional distribution","volume":"45","author":"Kushner","year":"2000","journal-title":"IEEE T. Automat. Contr."},{"key":"ref_50","unstructured":"Pourmohamad, T., and Lee, H.K.H. (2019). The Statistical Filter Approach to Constrained Optimization. Technometrics, 1\u201310."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.measurement.2018.03.020","article-title":"A voxel-based multiscale morphological airborne lidar filtering algorithm for digital elevation models for forest regions","volume":"123","author":"Liu","year":"2018","journal-title":"Measurement"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, L., Li, D., Yang, F., and Liu, Y. (2017). A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation. Remote Sens., 9.","DOI":"10.20944\/preprints201703.0178.v1"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0165-0114(99)00110-4","article-title":"Detecting homogeneous groups in clustering using the Euclidean distance","volume":"120","author":"Cadenas","year":"2001","journal-title":"Fuzzy Set. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11280-013-0211-y","article-title":"Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution","volume":"17","author":"Shaikh","year":"2014","journal-title":"World Wide Web"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1467-8659.2007.01016.x","article-title":"Efficient RANSAC for point-cloud shape detection","volume":"26","author":"Schnabel","year":"2007","journal-title":"Comput. Graph. Forum."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TLA.2017.7854627","article-title":"Cattle Brand Recognition using Convolutional Neural Network and Support Vector Machines","volume":"15","author":"Silva","year":"2017","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_57","first-page":"7","article-title":"Optimal affine approximation of image projective transformation","volume":"33","author":"Konovalenko","year":"2019","journal-title":"Sens. Sist."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s11629-016-3950-2","article-title":"Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning","volume":"14","author":"Lu","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2018.12.008","article-title":"Ridesharing car detection by transfer learning","volume":"273","author":"Wang","year":"2019","journal-title":"Artif. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Li, H., Zhang, Y., and Li, M. (2014). Instance Transfer Learning with Multisource Dynamic TrAdaBoost. Sci. World J.","DOI":"10.1155\/2014\/282747"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compag.2019.03.010","article-title":"A bilateral symmetry based pose normalization framework applied to livestock body measurement in point clouds","volume":"160","author":"Guo","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sun, Y., Li, L., Zheng, L., Hu, J., Li, W., Jiang, Y., and Yan, C. (2019). Image Classification base on PCA of Multi-view Deep Representation. arXiv.","DOI":"10.1016\/j.jvcir.2019.05.016"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1017\/S1751731118003348","article-title":"Estimation of genetic parameters for BW and body measurements in Brahman cattle","volume":"13","author":"Kamprasert","year":"2019","journal-title":"Animal"},{"key":"ref_64","first-page":"52","article-title":"Curvature-direction measures for 3D feature detection","volume":"9","author":"Li","year":"2013","journal-title":"Sci. China Inform. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1109\/TCSVT.2018.2866866","article-title":"Mean Curvature Is a Good Regularization for Image Processing","volume":"29","author":"Gong","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/S0167-8396(00)00006-6","article-title":"On surface normal and Gaussian curvature approximations given data sampled from a smooth surface","volume":"17","author":"Meek","year":"2000","journal-title":"Comput. Aided Geom. Des."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10851-017-0728-2","article-title":"Principal Curvature Measures Estimation and Application to 3D Face Recognition","volume":"59","author":"Tang","year":"2017","journal-title":"J. Math. Imaging Vis."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2005.02.006","article-title":"Least squares 3D surface and curve matching","volume":"59","author":"Gruen","year":"2005","journal-title":"ISPRS J. Photogramm."},{"key":"ref_69","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-ICRA, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5046\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:35:45Z","timestamp":1760189745000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,19]]},"references-count":69,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19225046"],"URL":"https:\/\/doi.org\/10.3390\/s19225046","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,19]]}}}