Today, the combination of Aerial and Terrestrial photos has been more implemented for 3D modelling purposes. This 3D modelling technique getting popular because it is supporting with photogrammetry structure from motion algorithm (SFM). The SFM algorithm makes automation in the processing step. One of the main problems that will occur in the automation of 3D modelling objects with the SFM algorithm is whether objects have texture-less surfaces. The purpose of this research is to evaluate some enhancement processes that were applied before running the SFM algorithm for 3D modelling. Some pre-processing enhancements are a combination Contrast-Limited Adaptive Histogram EqualizationAlgorithmContrast-Limited Adaptive Histogram Equalization (CLAHE) from Fiji-ImageJ and JPEG to RAW Ai artefact algorithm from Topaz Labs. Two sample objects are tested which are a heritage object that has a texture-less wall surface object and a paddy field that has a similar object pattern. Some aerial and terrestrial photos have been enhanced before processing in 3D modelling. The result shows that applying preprocessing enhancement can improve the completeness of the object, especially in texture-less wall surface area. Pre-processing enhancement improves the geometric accuracy and number of vertex and surfaces also. In the future, the combination of the Jpeg to Raw Ai and the CLAHE enhancement should be explored for the best 3D model solution.


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    Titel :

    Some Enhancement of Aerial and Terrestrial Photo for 3D Modeling of Texture-Less Object Surface


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Jain, Kamal (Herausgeber:in) / Mishra, Vishal (Herausgeber:in) / Pradhan, Biswajeet (Herausgeber:in) / Rokhmana, Catur Aries (Autor:in) / Fauzi, Hanif Muhammad (Autor:in)

    Kongress:

    International Conference on Unmanned Aerial System in Geomatics ; 2021 ; Roorkee, India April 02, 2021 - April 04, 2021



    Erscheinungsdatum :

    2023-03-16


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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