UAV photogrammetric survey and Image-Based elaborations for an Industrial Plant

被引:1
|
作者
Sanseverino, Anna [1 ]
Limongiello, Marco [2 ]
Fiorillo, Fausta [3 ]
机构
[1] Univ Pavia, DICAr, Pavia, Italy
[2] Univ Salerno, Dept Civil Engn, Fisciano, Italy
[3] Polytechn Milan, Milan, Italy
关键词
Integrated Survey; HBIM; Physically Based Rendering; Real-time Rendering; Industrial Archaeology; BIM;
D O I
10.20365/disegnarecon.29.2022.15
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The proposed application of the HBIM methodology for digitising a productive-industrial structure is based on the integration of data from different sources. An aerial photogrammetric survey (Unmanned Aerial Vehicle - UAV) was considered the most appropriate technique for the case. Therefore, a Scan-to-BIM modelling was carried out, keeping in mind a subsequent texturisation of the smart objects employing the photogrammetric images obtained from the UAV survey. Currently, applying the BIM methodology to the built environment is still a challenge; indeed, three-dimensional modelling based on survey point clouds is not automatic. Any BIM software is designed for new constructions, whereas the existing Heritage is characterised by unique and distinctive shapes, where each element has a specific and variable inclination, shape and thickness; therefore, it is necessary to adapt the available tools. Creating intelligent??parametric objects capable of representing the unique and singular shapes and geometries of historic architecture is a significant challenge of HBIM modelling. A workflow for the acquisition, processing and management of the survey data and the consequent modelling in a BIM environment of a disused industrial plant previously used as a tobacco factory was formalised. The aim was, therefore, to develop a model that is as close as possible to the real one and, at the same time, still keeps the informative aspects in order to promote the conservation and possible refurbishment of the cultural heritage through the use of photorealistic visualisation tools in real-time. The results confirm the proposed strategy hypotheses and seem to lead to promising future developments.
引用
收藏
页码:D1 / D10
页数:10
相关论文
共 50 条
  • [21] AN ADAPTIVE SCHEME FOR IMAGE-BASED VISUAL SERVOING OF AN UNDERACTUATED UAV
    Asl, Hamed Jabbari
    Oriolo, Giuseppe
    Bolandi, Hossein
    INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 2014, 29 (01): : 92 - 104
  • [22] Sequence Matching for Image-Based UAV-to-Satellite Geolocalization
    Wang, Zhen
    Shi, Dianxi
    Qiu, Chunping
    Jin, Songchang
    Li, Tongyue
    Shi, Yanyan
    Liu, Zhe
    Qiao, Ziteng
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 15
  • [23] Image-based Position Estimation of UAV using Kalman Filter
    Kojima, Takaaki
    Namerikawa, Toru
    2015 IEEE CONFERENCE ON CONTROL AND APPLICATIONS (CCA 2015), 2015, : 406 - 411
  • [24] Image-Based Virtual Try-On: A Survey
    Song, Dan
    Zhang, Xuanpu
    Zhou, Juan
    Nie, Weizhi
    Tong, Ruofeng
    Kankanhalli, Mohan
    Liu, An-An
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2024, : 2692 - 2720
  • [25] Image-based methods for dietary assessment: a survey
    Shumei Zhang
    Victor Callaghan
    Yan Che
    Journal of Food Measurement and Characterization, 2024, 18 : 727 - 743
  • [26] A Survey on Image-Based Hair Modeling Technique
    Bao Y.
    Qi Y.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2018, 55 (11): : 2543 - 2556
  • [27] Image-based methods for dietary assessment: a survey
    Zhang, Shumei
    Callaghan, Victor
    Che, Yan
    JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION, 2024, 18 (01) : 727 - 743
  • [28] Survey of image-based representations and compression techniques
    Shum, HY
    Kang, SB
    Chan, SC
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2003, 13 (11) : 1020 - 1037
  • [29] Image-Based Quantification of Plant Immunity and Disease
    Laflamme, Bradley
    Middleton, Maggie
    Lo, Timothy
    Desveaux, Darrell
    Guttman, David S.
    MOLECULAR PLANT-MICROBE INTERACTIONS, 2016, 29 (12) : 919 - 924
  • [30] Deep Learning in Image-Based Plant Phenotyping
    Murphy, Katherine M.
    Ludwig, Ella
    Gutierrez, Jorge
    Gehan, Malia A.
    ANNUAL REVIEW OF PLANT BIOLOGY, 2024, 75 : 771 - 795