ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 145-152, 2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
28 May 2014
Image pre-processing for optimizing automated photogrammetry performances
G. Guidi, S. Gonizzi, and L. L. Micoli Department of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, Italy
Keywords: Automated Photogrammetry, SFM, Image Matching, Polarizing filter, HDR, Tone mapping, Shiny materials, Dark materials, Cultural Heritage, image-based modeling performances Abstract. The purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materials. Because of the automatic detection of homologous points, the presence of highlights due to shiny materials, or nearly uniform dark patches produced by low reflectance materials, may produce erroneous matching involving wrong 3D point estimations, and consequently holes and topological errors on the mesh originated by the associated dense 3D cloud. This is due to the limited dynamic range of the 8 bit digital images that are matched each other for generating 3D data. The same 256 levels can be more usefully employed if the actual dynamic range is compressed, avoiding luminance clipping on the darker and lighter image areas. Such approach is here considered both using optical filtering and HDR processing with tone mapping, with experimental evaluation on different Cultural Heritage objects characterized by non-cooperative optical behavior. Three test images of each object have been captured from different positions, changing the shooting conditions (filter/no-filter) and the image processing (no processing/HDR processing), in order to have the same 3 camera orientations with different optical and digital pre-processing, and applying the same automated process to each photo set.
Conference paper (PDF, 1336 KB)

Citation: Guidi, G., Gonizzi, S., and Micoli, L. L.: Image pre-processing for optimizing automated photogrammetry performances, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 145-152, doi:10.5194/isprsannals-II-5-145-2014, 2014.

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