Geometry Fidelity for Spherical Images

MCML Authors

Zeynep Akata

Prof. Dr.

Principal Investigator

Interpretable and Reliable Machine Learning

Abstract

Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fréchet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID.

inproceedings

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ECCV 2024

18th European Conference on Computer Vision. Milano, Italy, Sep 29-Oct 04, 2024.

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Authors

A. Christensen • N. Mojab • K. Patel • K. Ahuja • Z. Akata • O. Winther • O. Gonzalez-Franco • A. Colaco

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Links

DOI

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Research Area

 B1 | Computer Vision