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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Geometry fidelity for spherical images.
In: (Computer Vision – ECCV 2024). Berlin [u.a.]: Springer, 2024. 276-292 (Lect. Notes Comput. Sc. ; 15138)
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 Frechet 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.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Spherical Image; Fidelity; Quality Evaluation; Cubemaps
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Computer Vision – ECCV 2024
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 15138,
Seiten: 276-292
Verlag
Springer
Verlagsort
Berlin [u.a.]