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iDFD: A Dataset Annotated for Depth and Defocus

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Image Analysis (SCIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13885))

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Abstract

Depth estimation and image deblurring from a single defocused image are fundamental tasks in Computer Vision (CV). Many methods have previously been proposed to solve these two tasks separately, using Deep Learning (DL) powerful learning capability. However, when it comes to training the Deep Neural Networks (DNN) for image deblurring or Depth from Defocus (DFD), the mentioned methods are mostly based on synthetic training datasets because of the difficulty of densely labeling depth and defocus on real images. The performance of the networks trained on synthetic data may deteriorate rapidly on real images. In this work, we present Indoor Depth from Defocus (iDFD), a Depth And Defocus Annotated dataset, which contains naturally defocused, All-in-Focus (AiF) images and dense depth maps of indoor environments. iDFD is the first public dataset to contain natural defocus and corresponding depth obtained using two appropriate sensors, DSLR and MS-Kinect camera. This dataset can support the development of DL based methods for depth estimation from defocus and image deblurring by providing the possibility to train the networks on real data instead of synthetic data. The dataset is available for download at iDFD.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 860370.

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Author information

Authors and Affiliations

  1. CEOSpaceTech, University POLITEHNICA of Bucharest (UPB), Bucharest, Romania

    Saqib Nazir & Daniela Coltuc

  2. CINTECX, Universidade de Vigo, Applied Geotechnology Group, Vigo, Spain

    Zhouyan Qiu, Joaquín Martínez-Sánchez & Pedro Arias

  3. ICT and Innovation Department, Ingeniería Insitu, Vigo, Spain

    Zhouyan Qiu

Authors
  1. Saqib Nazir
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  2. Zhouyan Qiu
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  3. Daniela Coltuc
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  4. Joaquín Martínez-Sánchez
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  5. Pedro Arias
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Corresponding author

Correspondence to Saqib Nazir .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Rikke Gade

  2. Linköping University, Linköping, Sweden

    Michael Felsberg

  3. Tampere University, Tampere, Finland

    Joni-Kristian Kämäräinen

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Nazir, S., Qiu, Z., Coltuc, D., Martínez-Sánchez, J., Arias, P. (2023). iDFD: A Dataset Annotated for Depth and Defocus. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13885. Springer, Cham. https://doi.org/10.1007/978-3-031-31435-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-31435-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31434-6

  • Online ISBN: 978-3-031-31435-3

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