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A Quality-Based Criteria for Efficient View Selection

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Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2077))

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Abstract

The generation of complete 3D models of real-world objects is a well-known problem. The accuracy of a reconstruction can be defined as the fidelity to the original model, but in the context of the 3D reconstruction, the ground truth model is usually unavailable. In this paper, we propose to evaluate the quality of the model through local intrinsic metrics, that reflect the quality of the current reconstruction based on geometric measures of the reconstructed model. We then show how those metrics can be embedded in a Next Best View (NBV) framework as additional criteria for selecting optimal views that improve the quality of the reconstruction. Tests performed on simulated data and synthetic images show that using quality metrics helps the NBV algorithm to focus the view selection on the poor-quality parts of the reconstructed model, thus improving the overall quality.

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Authors and Affiliations

  1. University of Toulouse, IRIT – Toulouse INP, Toulouse, France

    Rémy Alcouffe, Sylvie Chambon, Géraldine Morin & Simone Gasparini

Authors
  1. Rémy Alcouffe
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  2. Sylvie Chambon
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  3. Géraldine Morin
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  4. Simone Gasparini
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Corresponding author

Correspondence to Rémy Alcouffe .

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Editors and Affiliations

  1. Polytechnic Institute of Setubal, Setúbal, Portugal

    Joaquim Filipe

  2. Biomimetics and Intelligent Systems, University of Oulu, OULU, Finland

    Juha Röning

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Alcouffe, R., Chambon, S., Morin, G., Gasparini, S. (2024). A Quality-Based Criteria for Efficient View Selection. In: Filipe, J., Röning, J. (eds) Robotics, Computer Vision and Intelligent Systems. ROBOVIS 2024. Communications in Computer and Information Science, vol 2077. Springer, Cham. https://doi.org/10.1007/978-3-031-59057-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-59057-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-59056-6

  • Online ISBN: 978-3-031-59057-3

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