@inproceedings{55e15f7174494e8bbdd5b97aa2c4af44,
title = "Geometry Fidelity for Spherical Images",
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 Fr{\'e}chet 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.",
keywords = "Cubemaps, Fidelity, Quality Evaluation, Spherical Image",
author = "Anders Christensen and Nooshin Mojab and Khushman Patel and Karan Ahuja and Zeynep Akata and Ole Winther and Mar Gonzalez-Franco and Andrea Colaco",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",
year = "2025",
doi = "10.1007/978-3-031-72989-8_16",
language = "English",
isbn = "9783031729881",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "276--292",
editor = "Ale{\v s} Leonardis and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and G{\"u}l Varol",
booktitle = "Computer Vision – ECCV 2024 - 18th European Conference, Proceedings",
}