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Synthesizing Point Cloud Data Set for Historical Dome Systems

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Computer-Aided Architectural Design. Design Imperatives: The Future is Now (CAAD Futures 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1465))

Abstract

This paper offers a workflow for generating synthetic point cloud data sets to be used in deep learning algorithms in tasks of modeling historical architectural elements. Documentation of cultural heritage is a time-consuming process that requires high precision. Computational and semi-automatic tools enhance conventional methods to shorten the duration of the documentation phase and increase the accuracy of the output. Photogrammetry and laser scanning are how geometrical data is acquired and delivered as a point cloud with position, color, and optionally normal vector information. Segmenting architectural elements based on our interpretations of this data is possible using deep neural networks but is limited when, despite the millions of points from one building, the data is insufficient in terms of variance and quantity. To overcome this limitation, we propose a semi-automatic synthetic data set generation using parametric definitions of historic architectural elements. We create a synthetic dataset, namely the Historical Dome Dataset (HDD), consisting of nearly 1000 dome systems with four semantic classes. We quantitatively and qualitatively analyze the usefulness of the HDD by training a number of modern neural networks on it. Our method of synthesizing point clouds can quickly be adapted into similar cultural heritage projects to prepare relevant data to accurately train deep neural networks and process the collected cultural heritage data.

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Notes

  1. 1.

    https://github.com/HistoricDomeDataset/HistoricDomeDataset.

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Acknowledgements

This work is supported by TÜBİTAK (The Scientific and Technological Research Council of Turkey) Project Number: 119K896. A very special thanks to Demircan Taş and Berkay Öztürk for providing photogrammetric data. Lastly, we would like to thank our research group for their valuable discussions.

Author information

Authors and Affiliations

  1. Architectural Design Computing Graduate Program, Istanbul Technical University, 34437, Istanbul, Turkey

    Mustafa Cem Güneş

  2. Faculty of Computer and Informatics Engineering, Computer Engineering, Istanbul Technical University, 34469, Istanbul, Turkey

    Alican Mertan, Yusuf H. Sahin & Gozde Unal

  3. Department of Architecture, Istanbul Technical University, 34437, Istanbul, Turkey

    Mine Özkar

Authors
  1. Mustafa Cem Güneş
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  2. Alican Mertan
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  3. Yusuf H. Sahin
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  4. Gozde Unal
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  5. Mine Özkar
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Corresponding author

Correspondence to Mustafa Cem Güneş .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    David Gerber

  2. University of Southern California, Los Angeles, CA, USA

    Evangelos Pantazis

  3. Florida International University, Miami, FL, USA

    Biayna Bogosian

  4. University of Calgary, Calgary, Canada

    Alicia Nahmad

  5. Aalto University, Espoo, Finland

    Constantinos Miltiadis

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Güneş, M.C., Mertan, A., Sahin, Y.H., Unal, G., Özkar, M. (2022). Synthesizing Point Cloud Data Set for Historical Dome Systems. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_33

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  • DOI: https://doi.org/10.1007/978-981-19-1280-1_33

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