{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:52:51Z","timestamp":1781650371559,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["338647"],"award-info":[{"award-number":["338647"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work investigates the properties of 3D imaging in turbid water in context of being able to precisely remove false 3D measurements that are a result of scattering. Such false measurements make object boundaries highly uncertain and limit the usefulness of 3D data. The work is motivated by a need for high-quality 3D image data for underwater robotics, also in turbid waters, where false 3D data can make 3D point cloud algorithms complex and unreliable. Basing ourselves on triangulation-based 3D imaging in turbid water, we show through first-principles analysis how a combination of certain camera\/projector geometries and projected patterns allows for the reliable discrimination between true and false 3D measurements. We present an efficient (O1) algorithm capable of rapidly performing this discrimination. Through experiments, we show that we can provide a 50\u2013100\u00d7 better false positive rate than single-camera algorithms across relevant turbidities, whilst maintaining an almost zero false negative rate. This combination of camera hardware, projected patterns, and algorithms gives a significant robustness improvement of subsea 3D imaging systems, enabling robust and trustworthy 3D imaging.<\/jats:p>","DOI":"10.3390\/s25237250","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T09:56:02Z","timestamp":1764323762000},"page":"7250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["3D Point Cloud Verification and Scatter Removal Using a Dual Camera\/Projector Setup"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4019-173X","authenticated-orcid":false,"given":"Jens T.","family":"Thielemann","sequence":"first","affiliation":[{"name":"SINTEF AS, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0748-9246","authenticated-orcid":false,"given":"Jostein","family":"Thorstensen","sequence":"additional","affiliation":[{"name":"SINTEF AS, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","unstructured":"Market Research Future (2025, November 19). Underwater Robotics Market Size Share 2030|Industry Trends. Available online: https:\/\/www.marketresearchfuture.com\/reports\/underwater-robotics-market-7605."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hu, K., Wang, T., Shen, C., Weng, C., Zhou, F., Xia, M., and Weng, L. (2023). Overview of Underwater 3D Reconstruction Technology Based on Optical Images. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11050949"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/JDT.2010.2066546","article-title":"Three-Dimensional Visualization of Objects in Turbid Water Using Integral Imaging","volume":"6","author":"Cho","year":"2010","journal-title":"J. Disp. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.1364\/OL.43.003261","article-title":"Optical sensing and detection in turbid water using multidimensional integral imaging","volume":"43","author":"Komatsu","year":"2018","journal-title":"Opt. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1364\/JOSAA.27.000461","article-title":"Distortion-tolerant 3D recognition of underwater objects using neural networks","volume":"27","author":"Schulein","year":"2010","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Levy, D., Peleg, A., Pearl, N., Rosenbaum, D., Akkaynak, D., Korman, S., and Treibitz, T. (2023, January 17\u201324). SeaThru-NeRF: Neural Radiance Fields in Scattering Media. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00014"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wen, B., Trepte, M., Aribido, J., Kautz, J., Gallo, O., and Birchfield, S. (2025, January 11\u201315). FoundationStereo: Zero-Shot Stereo Matching. Proceedings of the IEEE Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.00495"},{"key":"ref_8","unstructured":"Min, J., Jeon, Y., Kim, J., and Choi, M. (2025, January 19\u201323). S2M2: Scalable Stereo Matching Model for Reliable Depth Estimation. Proceedings of the IEEE\/CVF International Conference on Computer Vision 2025, Honolulu, HI, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Castill\u00f3n, M., Palomer, A., Forest, J., and Ridao, P. (2019). State of the Art of Underwater Active Optical 3D Scanners. Sensors, 19.","DOI":"10.3390\/s19235161"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"33911","DOI":"10.1364\/OE.23.033911","article-title":"Underwater depth imaging using time-correlated single-photon counting","volume":"23","author":"Maccarone","year":"2015","journal-title":"Opt. Express"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.optlastec.2010.11.007","article-title":"Three-dimensional shape measurement for an underwater object based on two-dimensional grating pattern projection","volume":"43","author":"Zhang","year":"2011","journal-title":"Opt. Laser Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Massot-Campos, M., Oliver-Codina, G., Kemal, H., Petillot, Y., and Bonin-Font, F. (2015, January 18\u201321). Structured light and stereo vision for underwater 3D reconstruction. Proceedings of the OCEANS 2015, Genova, Italy.","DOI":"10.1109\/OCEANS-Genova.2015.7271433"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1007\/s11802-021-4751-y","article-title":"Estimation of the Scale of Artificial Reef Sets on the Basis of Underwater 3D Reconstruction","volume":"20","author":"Tang","year":"2021","journal-title":"J. Ocean Univ. China"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Narasimhan, S.G., Nayar, S.K., Sun, B., and Koppal, S.J. (2005, January 17\u201321). Structured light in scattering media. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905), Beijing, China.","DOI":"10.1109\/ICCV.2005.232"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107344","DOI":"10.1016\/j.optlaseng.2022.107344","article-title":"Structured light-based underwater 3-D reconstruction techniques: A comparative study","volume":"161","author":"Lyu","year":"2023","journal-title":"Opt. Lasers Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.isprsjprs.2011.02.009","article-title":"Experimentation of structured light and stereo vision for underwater 3D reconstruction","volume":"66","author":"Bruno","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Risholm, P., Kirkhus, T., Thielemann, J.T., and Thorstensen, J. (2019). Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements. Sensors, 19.","DOI":"10.3390\/s19051043"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1088\/0957-0233\/10\/12\/307","article-title":"Three-dimensional measurement of underwater work sites using structured laser light","volume":"10","author":"Tetlow","year":"1999","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1364\/AO.57.003927","article-title":"Real-time super-resolved 3D in turbid water using a fast range-gated CMOS camera","volume":"57","author":"Risholm","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45013","DOI":"10.1364\/OE.539546","article-title":"Light transport in turbid water for 3D underwater imaging","volume":"32","author":"Thorstensen","year":"2024","journal-title":"Opt. Express"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Risholm, P., Thielemann, J.T., Moore, R., and Haugholt, K.H. (2018, January 22\u201325). A scatter removal technique to enhance underwater range-gated 3D and intensity images. Proceedings of the OCEANS 2018 MTS\/IEEE Charleston, Charleston, SC, USA.","DOI":"10.1109\/OCEANS.2018.8604613"},{"key":"ref_22","unstructured":"Bass, M. (2010). Optical Properties of Water. Handbook of Optics: Volume IV\u2014Optical Properties of Materials, Nonlinear Optics, Quantum Optics, McGraw-Hill Education."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.patcog.2015.09.023","article-title":"Generation of fiducial marker dictionaries using Mixed Integer Linear Programming","volume":"51","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_24","unstructured":"Zhang, Z. (1999, January 20\u201325). Flexible camera calibration by viewing a plane from unknown orientations. Proceedings of the Seventh IEEE International Conference on Computer Vision, Corfu, Greece."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Bengtson, S.H., Gade, R., Madsen, N., and Moeslund, T.B. (2018, January 18\u201322). Camera Calibration for Underwater 3D Reconstruction Based on Ray Tracing Using Snell\u2019s Law. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00190"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1109\/ICCV.1999.791262","article-title":"A simple and efficient rectification method for general motion","volume":"Volume 1","author":"Pollefeys","year":"1999","journal-title":"Proceedings of the Seventh IEEE International Conference on Computer Vision"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun. ACM"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27758","DOI":"10.1364\/OE.25.027758","article-title":"Design tool for TOF and SL based 3D cameras","volume":"25","author":"Bouquet","year":"2017","journal-title":"Opt. Express"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:35:37Z","timestamp":1764956137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":29,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25237250"],"URL":"https:\/\/doi.org\/10.3390\/s25237250","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]}}}