Image pre-processing for optimizing automated photogrammetry performances
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 materia...
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doaj-4c3df0f119f7407e8fef50f9633a17352020-11-24T20:49:15ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502014-05-01II-514515210.5194/isprsannals-II-5-145-2014Image pre-processing for optimizing automated photogrammetry performancesG. Guidi0S. Gonizzi1L. L. Micoli2Department of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyThe 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.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/145/2014/isprsannals-II-5-145-2014.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
G. Guidi S. Gonizzi L. L. Micoli |
spellingShingle |
G. Guidi S. Gonizzi L. L. Micoli Image pre-processing for optimizing automated photogrammetry performances ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
G. Guidi S. Gonizzi L. L. Micoli |
author_sort |
G. Guidi |
title |
Image pre-processing for optimizing automated photogrammetry performances |
title_short |
Image pre-processing for optimizing automated photogrammetry performances |
title_full |
Image pre-processing for optimizing automated photogrammetry performances |
title_fullStr |
Image pre-processing for optimizing automated photogrammetry performances |
title_full_unstemmed |
Image pre-processing for optimizing automated photogrammetry performances |
title_sort |
image pre-processing for optimizing automated photogrammetry performances |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2014-05-01 |
description |
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. |
url |
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/145/2014/isprsannals-II-5-145-2014.pdf |
work_keys_str_mv |
AT gguidi imagepreprocessingforoptimizingautomatedphotogrammetryperformances AT sgonizzi imagepreprocessingforoptimizingautomatedphotogrammetryperformances AT llmicoli imagepreprocessingforoptimizingautomatedphotogrammetryperformances |
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