August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Cardinal viewpoints of 3D objects predicted by 2D optical flow model
Author Affiliations & Notes
  • Emma E.M. Stewart
    Justus-Liebig University Giessen, Germany
  • Roland W. Fleming
    Justus-Liebig University Giessen, Germany
    Center for Mind, Brain and Behavior, Universities of Marburg and Giessen, Germany
  • Alexander C. Schütz
    University of Marburg, Germany
    Center for Mind, Brain and Behavior, Universities of Marburg and Giessen, Germany
  • Footnotes
    Acknowledgements  This project was supported by the Deutsche Forschungsgemeinschaft, through project numbers 460533638 and 222641018–SFB/TRR-135 TP C1, and by the Research Cluster “The Adaptive Mind”, funded by the Hessian Ministry for Higher Education, Research, Science, and the Arts.
Journal of Vision August 2023, Vol.23, 4724. doi:
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    • Get Citation

      Emma E.M. Stewart, Roland W. Fleming, Alexander C. Schütz; Cardinal viewpoints of 3D objects predicted by 2D optical flow model. Journal of Vision 2023;23(9):4724.

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      © ARVO (1962-2015); The Authors (2016-present)

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Humans have the remarkable ability to determine which viewpoints of 3D objects are qualitatively distinct or special. Seeing an object from such viewpoints can benefit object recognition and recall. In particular, the front, back and side views—sometimes referred to as cardinal viewpoints—are often particularly informative, yet there is a lack of formal, quantitative models to predict which viewpoints are qualitatively distinct and explain what makes them special. Here we compared human discrimination judgements to predictions from a 2D optical-flow model to predict cardinal viewpoints of 3D objects. We tested human discrimination performance at cardinal (front, back) and non-cardinal viewpoints of 35 familiar objects. Participants were shown a base (cardinal or non-cardinal) viewpoint alongside a rotated viewpoint (0, 5, 10, 15 degrees), and indicated whether the two views were the same or different. For n = 100 participants, we found a marked benefit in viewpoint discrimination when the base viewpoint was cardinal, with some variability between objects. We reasoned that a 2D optical-flow model that can predict human viewpoint dissimilarity judgements (Stewart et al, 2022) may also explain the variability in this data. We found that the model could explain human discrimination performance, and could differentiate cardinal from non-cardinal viewpoints. To verify that these findings generalize to non-familiar objects—for which recognizable indicators of the front and back (e.g., face, tail) are absent—we created 10 novel 3D objects, and participants (n = 50) indicated the “front” viewpoint. The model could predict which viewpoints were most likely to be chosen as the front, even with unfamiliar objects. This study shows that a 2D model can predict the cardinal viewpoints of a 3D object, and explain variance in human viewpoint discrimination performance at cardinal and non-cardinal viewpoints. This provides a quantitative method to define qualitatively special viewpoints of 3D objects.


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