{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:28:52Z","timestamp":1778354932910,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agriculture and Food Research Initiative Competitive","award":["2021-67021-33448"],"award-info":[{"award-number":["2021-67021-33448"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies.<\/jats:p>","DOI":"10.3390\/s24165275","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T03:49:36Z","timestamp":1723693776000},"page":"5275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Gbenga","family":"Omotara","sequence":"first","affiliation":[{"name":"Vision-Guided and Intelligent Robotics Laboratory, Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0193-7391","authenticated-orcid":false,"given":"Seyed Mohamad Ali","family":"Tousi","sequence":"additional","affiliation":[{"name":"Vision-Guided and Intelligent Robotics Laboratory, Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5685-7343","authenticated-orcid":false,"given":"Jared","family":"Decker","sequence":"additional","affiliation":[{"name":"Division of Animal Sciences, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5564-8301","authenticated-orcid":false,"given":"Derek","family":"Brake","sequence":"additional","affiliation":[{"name":"Division of Animal Sciences, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. N.","family":"DeSouza","sequence":"additional","affiliation":[{"name":"Vision-Guided and Intelligent Robotics Laboratory, Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., Rod\u00e9s-Guirao, L., Mathieu, E., Gerber, M., Ortiz-Ospina, E., Hasell, J., and Roser, M. (2024, June 14). Population Growth. Our World in Data. Available online: https:\/\/ourworldindata.org\/population-growth."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.compag.2019.01.019","article-title":"High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits","volume":"157","author":"Allain","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","first-page":"222","article-title":"Three-Dimensional Shape Measurement System for Black Cattle Using KINECT Sensor","volume":"7","author":"Kawasue","year":"2013","journal-title":"Int. J. Circuits Syst. Signal Process."},{"key":"ref_4","unstructured":"Yoshida, K., and Kawasue, K. (2014, January 24\u201328). Compact three-dimensional vision for ubiquitous sensing. Proceedings of the UBICOMM 2014\u20148th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Rome, Italy."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ruchay, A.N., Dorofeev, K.A., Kalschikov, V.V., Kolpakov, V.I., and Dzhulamanov, K.M. (2019). Accurate 3D shape recovery of live cattle with three depth cameras. IOP Conf. Ser. Earth Environ. Sci., 341.","DOI":"10.1088\/1755-1315\/341\/1\/012147"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Ma, W., Li, Q., Zhao, C., Tulpan, D., Yang, S., Ding, L., Gao, R., Yu, L., and Wang, Z. (2022). Multi-view real-time acquisition and 3D reconstruction of point clouds for beef cattle. Comput. Electron. Agric., 197.","DOI":"10.1016\/j.compag.2022.106987"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Sabov, A., and Kr\u00fcger, J. (2008, January 21\u201323). Identification and correction of flying pixels in range camera data. Proceedings of the Spring Conference on Computer Graphics, Budmerice Castle, Slovakia.","DOI":"10.1145\/1921264.1921293"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_12","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"G\u00fcmeli, C., Dai, A., and Nie\u00dfner, M. (2023, January 18\u201322). ObjectMatch: Robust Registration using Canonical Object Correspondences. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01257"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, J., Zhou, Q.Y., and Koltun, V. (2017, January 22\u201329). Colored Point Cloud Registration Revisited. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.25"},{"key":"ref_15","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2018). Open3D: A Modern Library for 3D Data Processing. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Olson, E. (2011, January 9\u201313). AprilTag: A robust and flexible visual fiducial system. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979561"},{"key":"ref_17","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the KDD\u201996: Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_18","unstructured":"Kazhdan, M., Bolitho, M., and Hoppe, H. (2006, January 26\u201328). Poisson surface reconstruction. Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Cagliari, Sardinia, Italy."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2487228.2487237","article-title":"Screened poisson surface reconstruction","volume":"32","author":"Kazhdan","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TRO.2020.3033695","article-title":"TEASER: Fast and Certifiable Point Cloud Registration","volume":"37","author":"Yang","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2016). Fast Global Registration. Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46475-6_47"},{"key":"ref_22","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 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_23","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, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/TIT.1983.1056714","article-title":"On the shape of a set of points in the plane","volume":"29","author":"Edelsbrunner","year":"1983","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/2945.817351","article-title":"The ball-pivoting algorithm for surface reconstruction","volume":"5","author":"Bernardini","year":"1999","journal-title":"IEEE Trans. Vis. Comput. Graph."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5275\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:36:47Z","timestamp":1760110607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":26,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165275"],"URL":"https:\/\/doi.org\/10.3390\/s24165275","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.08.04.552010","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]}}}