Abstract
Abstract. Image based-modeling practices in the field of Cultural Heritage studies are nowadays no longer seen as one-shot applications but as various and complex multimodal scenarios. Current use of SFM and photogrammetric methods implies their extensions to facilitate the management of complex multimodal data sets carried-out by different experts around a single heritage asset. In order to fully benefit of collaborative semantic enrichment of spatially oriented resources, a versatile and robust solution have been developed to enable incremental registration of image-sets within the web-based platform AIOLI. For this purpose, this paper will present an on-going development of a Totally Automated Co-registration and Orientations (TACO) work-flow.
Highlights
Image-based modeling (IBM) state nowadays as the most used technique in CH field thanks to its versatility and lower cost compared to Range-based modeling
AIOLI is a reality-based 3D annotation platform designed for a multidisciplinary CH community to construct semantically enriched 3D descriptions of heritage assets starting from photogrammetric-friendly image set and spatialized annotations coupled with additional resources
In order to face the pre-requisite need of collaborative documentation of CH objects, we developed incremental processing mode within TACO willing to support the complexity of multimodal acquisitions scenarios currently performed in CH domain
Summary
Image-based modeling (IBM) state nowadays as the most used technique in CH field thanks to its versatility and lower cost compared to Range-based modeling It requires higher technical skill in data capture process while the data processing is still divided between the complexity of open-source packages and the opacity of commercial black-box solutions. Integrated as the spatial registration core engine, it has been develop to allow the 2D/3D propagation of semantic annotations among multiples resources within AIOLI collaborative web-platform. It has been conceived as a flexible and evolutive pipeline so as to support a global data fusion methodology aiming to merge complex multimodal acquisitions (i.e. multisensor, multi-scalar, multi-spectral and/or simultaneously multitemporal). This article present the first implementation for automated processing of CH imaging multimodal acquisitions based on previous photogrammetric fusion experiments (Pamart et al, 2016, Pamart et al, 2017)
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