Automatic Image Selection in Photogrammetric Multi-view Stereo Methods

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dc.contributor.authorHosseininaveh, Alien_US
dc.contributor.authorSerpico, Margareten_US
dc.contributor.authorRobson, Stuarten_US
dc.contributor.authorHess, Monaen_US
dc.contributor.authorBoehm, Janen_US
dc.contributor.authorPridden, Ivoren_US
dc.contributor.authorAmati, Giancarloen_US
dc.contributor.editorDavid Arnold and Jaime Kaminski and Franco Niccolucci and Andre Storken_US
dc.date.accessioned2013-11-08T10:32:37Z
dc.date.available2013-11-08T10:32:37Z
dc.date.issued2012en_US
dc.description.abstractThis paper brings together a team of specialists in optical metrology, museum curation, collection digitization and 3D development to describe and illustrate by example a method for the selection of the most suitable camera views, vantage viewpoints, from a large image dataset intended for metric 3D artefact reconstruction. The presented approach is capable of automatically identifying and processing the most appropriate images from a multi-image photogrammetric network captured by an imaging specialist. The aim is to produce a 3D model suited to a wide range of museum uses, including visitor interactives. The approach combines off-the-shelf imaging equipment with rigorous photogrammetric bundle adjustment and multi-view stereo (MVS), supported by an image selection process that is able to take into account range-related and visibility-related constraints. The paper focusses on the two key steps of image clustering and iterative image selection. The developed method is illustrated by the 3D recording of four ancient Egyptian artefacts from the Petrie Museum of Egyptian Archaeology at UCL, with an analysis taking into account completeness, coordination uncertainty and required number of images. Comparison is made against the baseline of the established CMVS (Clustering Views for Multi-view Stereo), which is a free package for selecting vantage images within a huge image collection. For the museum, key outputs from the 3D recording process are visitor interactives which are built around high quality textured mesh models. The paper therefore considers the quality of the output from each process as input to texture model generation. Results demonstrate that whilst both methods can provide high quality records, our new method, Image Network Designer (IND), can provide a better image selection for MVS than CMVS in terms of coordination uncertainty and completeness of the final model for the museum recording of artefacts. Furthermore, the improvements gained, particularly in model completeness, minimise the significant overhead in mesh editing needed to provide a more direct and economical route to 3D model output.en_US
dc.description.seriesinformationVAST: International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritageen_US
dc.identifier.isbn978-3-905674-39-2en_US
dc.identifier.issn1811-864Xen_US
dc.identifier.urihttps://doi.org/10.2312/VAST/VAST12/009-016en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectKeywordsen_US
dc.subjectcultural heritage objectsen_US
dc.subjectMulti View Stereo (MVS)en_US
dc.subjectStructure from Motion (SfM)en_US
dc.subjectClose Range Photogrammetryen_US
dc.subjectImaging Networken_US
dc.subjectImage Clustering and Selectionen_US
dc.subjectancient Egypten_US
dc.subjectcartonnageen_US
dc.subjectHawaraen_US
dc.subjectGuroben_US
dc.titleAutomatic Image Selection in Photogrammetric Multi-view Stereo Methodsen_US
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VAST12: The 13th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage