{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T02:32:10Z","timestamp":1778380330141,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2015,12,5]],"date-time":"2015-12-05T00:00:00Z","timestamp":1449273600000},"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>The objective of the work is to model the shape of the sinusoidal shape of regular water waves generated in a laboratory flume. The waves are traveling in time and render a smooth surface, with no white caps or foam. Two methods are proposed, treating the water as a diffuse and specular surface, respectively. In either case, the water is presumed to take the shape of a traveling sine wave, reducing the task of the 3D reconstruction to resolve the wave parameters. The first conceived method performs the modeling part purely in 3D space. Having triangulated the points in a separate phase via bundle adjustment, a sine wave is fitted into the data in a least squares manner. The second method presents a more complete approach for the entire calculation workflow beginning in the image space. The water is perceived as a specular surface, and the traveling specularities are the only observations visible to the  cameras, observations that are notably single image. The depth ambiguity is removed given additional constraints encoded within the law of reflection and the modeled parametric surface. The observation and constraint equations compose a single system of equations that is solved with the method of least squares adjustment. The devised approaches are validated against the data coming from a capacitive level sensor and on physical targets floating on the surface. The outcomes agree to a high degree.<\/jats:p>","DOI":"10.3390\/s151229828","type":"journal-article","created":{"date-parts":[[2015,12,9]],"date-time":"2015-12-09T07:06:30Z","timestamp":1449644790000},"page":"30784-30809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Sinusoidal Wave Estimation Using Photogrammetry and Short Video Sequences"],"prefix":"10.3390","volume":"15","author":[{"given":"Ewelina","family":"Rupnik","sequence":"first","affiliation":[{"name":"Department of Geodesy and Geoinformation, Technische Universit\u00e4t Wien, Gusshausstrasse 27-29, Vienna 1040, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josef","family":"Jansa","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, Technische Universit\u00e4t Wien, Gusshausstrasse 27-29, Vienna 1040, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2348-7929","authenticated-orcid":false,"given":"Norbert","family":"Pfeifer","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, Technische Universit\u00e4t Wien, Gusshausstrasse 27-29, Vienna 1040, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,5]]},"reference":[{"key":"ref_1","unstructured":"Kohlsch\u00fctter, E. 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