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Evaluation of 3D Reconstruction Pipelines Under Varying Imaging Conditions

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Image Analysis and Processing – ICIAP 2023 (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14234))

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Abstract

We present IVL-SYNTHSFM-v2, a dataset of 4000 images depicting five objects from different viewpoints and under different lighting conditions and acquisition setups. The images have been rendered from 3D scenes with varying camera positions, lights, camera depth of field, and motion blur. Images depict one of five objects each acquired using eight different acquisition setups (scenes). 100 images have been rendered from each scene. The dataset is intended to be used to evaluate 3D reconstruction algorithms. The varying imaging conditions are introduced to challenge the algorithms to address realistic, non-ideal situations. The dataset provides pixel-precise ground truth to perform accurate evaluations. We demonstrate the usefulness of IVL-SYNTHSFM-v2 by assessing state-of-the-art 3D reconstruction algorithms.

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Notes

  1. 1.

    https://doi.org/10.17632/fnxy8z8894.

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Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Davide Marelli, Simone Bianco & Gianluigi Ciocca

Authors
  1. Davide Marelli
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  2. Simone Bianco
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  3. Gianluigi Ciocca
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Corresponding author

Correspondence to Davide Marelli .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Marelli, D., Bianco, S., Ciocca, G. (2023). Evaluation of 3D Reconstruction Pipelines Under Varying Imaging Conditions. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-43153-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43152-4

  • Online ISBN: 978-3-031-43153-1

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