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dc.contributor.authorMu, Danfengen_US
dc.contributor.authorPieras, Marcosen_US
dc.contributor.authorBroekens, Douween_US
dc.contributor.authorMarroquim, Ricardoen_US
dc.contributor.editorAgus, Marco and Garth, Christoph and Kerren, Andreasen_US
dc.date.accessioned2021-06-12T11:03:16Z
dc.date.available2021-06-12T11:03:16Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-143-4
dc.identifier.urihttps://doi.org/10.2312/evs.20211049
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20211049
dc.description.abstractWhile sailing, sailors rely on their eyes to inspect the sail shape and adjust the configurations to achieve an appropriate shape for a certain the weather condition. Mastering this so-called trimming process requires years of experience since the visual inspection of the sail shape suffers from inaccuracies and many times are difficult to communicate verbally. Therefore, this research proposes a visual analysis tool that presents an accurate sail shape representation and supports sailors in investigating the optimal sail shape for certain weather conditions. In order to achieve our goals, we reconstruct the 3D sail shape from point clouds acquired by photogrammetry methods. For incomplete acquisitions we deform a complete template sail to estimate the missing parts. We designed a visualization dashboard for sailors to explore the 3D structure, 2D profiles and characteristics of the time-varying sail shape as well as analyze their relation to boat speed. The usability of the visualization tool is tested through a qualitative evaluation with two sailing experts. The result shows that the reconstruction and deformation of sail shape are plausible. Furthermore, the visualization dashboard has the potential to enhance sailors' comprehension of sail shape and provide insights towards optimal trimming.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectInformation visualization
dc.titleSailVis: Reconstruction and Multifaceted Visualization of Sail Shapeen_US
dc.description.seriesinformationEuroVis 2021 - Short Papers
dc.description.sectionheadersMachine Learning and SciVis Applications
dc.identifier.doi10.2312/evs.20211049
dc.identifier.pages19-23


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